CN116542537B - Green space planning method, system, equipment and medium for increasing carbon sink - Google Patents

Green space planning method, system, equipment and medium for increasing carbon sink Download PDF

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CN116542537B
CN116542537B CN202310749569.7A CN202310749569A CN116542537B CN 116542537 B CN116542537 B CN 116542537B CN 202310749569 A CN202310749569 A CN 202310749569A CN 116542537 B CN116542537 B CN 116542537B
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green
green space
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CN116542537A (en
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郑曦
刘阳
欧小杨
吕英烁
艾昕
周凯
张雅茹
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Beijing Forestry University
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Beijing Forestry University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Abstract

The embodiment of the application provides a green space planning method, a system, electronic equipment, a medium and a program product for increasing carbon sink, wherein the method comprises the following steps: calculating the net carbon dioxide absorption of the green space of the basal period year and the carbon dioxide emission of the planning area in the basal period year, so as to determine the carbon offset capacity of the green space of the basal period year; acquiring a plurality of candidate lifting targets with carbon offset capability, and predicting the predicted carbon dioxide emission of a planning area in an optimized year; determining the number of candidate green spaces corresponding to the plurality of candidate lifting targets; determining the number of planned green spaces from the number of candidate green spaces according to the land constraint condition; according to the position of the green space in the basal period, determining candidate distribution areas of the green space in the optimized year, and planning the position and the number of the green space in the optimized year by combining with the number of the planned green spaces; the green space of the basal year and the optimal year is the green space of the planning area in the basal year and the optimal year respectively.

Description

Green space planning method, system, equipment and medium for increasing carbon sink
Technical Field
The embodiment of the application relates to the technical field of data processing, in particular to a green space planning method, a system, electronic equipment, a medium and a program product for increasing carbon sink.
Background
In the global carbon emission process, although the global city accounts for only 3% of the earth's surface, the carbon emission of the city accounts for about 75% of the global total carbon emission, making the city a focus for slowing down and adapting to climate change. Urban green space is the most important natural 'carbon warehouse' of the city, vegetation in the green space can promote carbon sink process (namely process of absorbing carbon dioxide in atmosphere), and carbon sink benefit can be remarkably improved by reasonably distributing the green space. However, rapid urban ization with high energy consumption not only leads to rapid increase of the gas emission of the isothermal chamber of carbon dioxide, but also encroaches on a large amount of urban green space, causes green space fragmentation and space heterogeneity, and has a significant negative effect on the carbon sink capacity of the urban ecological system. Therefore, a scientific planning method is needed to improve the carbon sequestration benefit of the green space.
The prior researches provide ideas for the synergy of the urban green space carbon sink from two aspects of quantity configuration and space layout, but most of the researches only pay attention to one aspect of quantity configuration or space layout, and the quantity configuration and the space layout are not optimally combined, so that the effect of increasing the carbon sink is poor on one side of the prior green space planning method, and the requirement of urban green space planning is difficult to meet.
In view of the foregoing, a technical problem to be solved is how to provide a green space planning method for increasing carbon sequestration, so as to be better applicable to planning of green space and enhance the carbon sequestration capacity of green space.
Disclosure of Invention
In view of the above, an embodiment of the present application provides a green space planning scheme to at least partially solve the above-mentioned problems.
According to a first aspect of an embodiment of the present application, there is provided a green space planning method for increasing carbon sink, including:
calculating the net carbon dioxide absorption of the green space of the basal period year and planning the basal period carbon dioxide emission of the area in the basal period year; determining a carbon offset capacity of a green space of a prime year based on the net carbon dioxide uptake and the prime carbon dioxide emissions; acquiring a plurality of candidate lifting targets of the carbon cancellation capability, and predicting predicted carbon dioxide emission of the planning area in an optimized year; determining the number of candidate green spaces respectively corresponding to the plurality of candidate lifting targets according to the predicted carbon dioxide emission and the carbon offset capability; determining the number of planned green spaces from the plurality of candidate green spaces according to the land constraint condition; according to the position of the green space in the basal period, determining candidate distribution areas of the green space in the optimized year; according to the number of the planned green spaces and the candidate distribution areas, planning the positions and the number of the green spaces in the optimized year; the green space of the basal period year is the green space of the planning area in the basal period year, and the green space of the optimization year is the green space of the planning area in the optimization year.
According to a second aspect of an embodiment of the present application, there is provided a green space planning system for increasing carbon sequestration, comprising: a carbon dioxide calculation unit for calculating a net carbon dioxide absorption amount of a green space of the base year and a base carbon dioxide emission amount of the planned area in the base year; a carbon offset capability calculation unit for determining a carbon offset capability of a green space of a base year based on the net carbon dioxide absorption amount and the base carbon dioxide emission amount; a candidate number determination unit configured to acquire a plurality of candidate lifting targets of the carbon offset capability, and predict a predicted carbon dioxide emission amount of the planned region in an optimization year; determining the number of candidate green spaces respectively corresponding to the plurality of candidate lifting targets according to the predicted carbon dioxide emission and the carbon offset capability; the planning quantity determining unit is used for determining the planning green space quantity from the candidate green space quantities according to the land constraint condition; a candidate distribution area determining unit for determining a candidate distribution area of the green space of the optimized year according to the position of the green space of the base year; the green space planning unit is used for planning the positions and the number of the green spaces in the optimized year according to the number of the planned green spaces and the candidate distribution areas; the green space of the basal period year is the green space of the planning area in the basal period year, and the green space of the optimization year is the green space of the planning area in the optimization year.
According to a third aspect of an embodiment of the present application, there is provided an electronic apparatus including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the method.
According to a fourth aspect of embodiments of the present application, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
According to the embodiment of the application, the number of the planned green spaces is determined from the number of the candidate green spaces according to the land constraint condition, the candidate distribution area for setting the green spaces in the optimized year is determined according to the positions of the green spaces in the base period, and the number configuration and the space layout are combined, so that the positions and the number of the green spaces in the optimized year can be scientifically and effectively planned, the carbon sink capacity of the green spaces is enhanced, and the requirement of urban green space planning is better met. In addition, the embodiment of the application can clearly optimize the number and the space position of the green space of the year by planning the position and the number of the green space of the year, and the urban ecological planning and construction of the year of the period of the foundation and the year of the optimization are carried out so as to improve the feasibility of the low-carbon development of the city.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a flowchart illustrating a green space planning method for increasing carbon sink according to an embodiment of the present application.
Fig. 2 is an overall flow chart of green space planning according to an embodiment of the application.
FIG. 3 is a statistical plot of carbon dioxide emissions for a planned area in accordance with an embodiment of the present application.
Fig. 4A is a diagram illustrating a biological migration resistance according to an embodiment of the present application.
Fig. 4B is a schematic view of ventilation resistance according to an embodiment of the present application.
FIG. 4C is a drag profile of a planned area in accordance with an embodiment of the present application.
Fig. 4D is a schematic diagram of a candidate distribution area according to an embodiment of the application.
Fig. 5 is a green space layout diagram according to an embodiment of the present application.
Fig. 6 is a block diagram of a green space planning system for increasing carbon sequestration in accordance with an embodiment of the present application.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions in the embodiments of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the present application, shall fall within the scope of protection of the embodiments of the present application.
The embodiments of the present application will be further described with reference to the accompanying drawings.
The embodiment of the application provides a green space planning method for increasing carbon sink, which at least partially solves the problems existing in the prior art, and is described below through a plurality of embodiments.
Referring to fig. 1 of the drawings, a schematic flow chart of a green space planning method for increasing carbon sink according to an embodiment of the present application is shown, and the method includes the following steps as shown in fig. 1.
S101: the net carbon dioxide uptake of the green space of the basal year and the basal carbon dioxide emission of the planned area in the basal year are calculated.
The basal year is the reference year for green space planning; the planning area is an area for green space planning, and may be an area in a province, a city, a county, or the like. Green space may be understood as land partially or completely covered by herbs, trees, shrubs, or other vegetation, and illustratively, green space may be divided into four land types, cultivated land, woodland, grassland, and unutilized land; the "green space" in the embodiment of the application is the green space of the planning area, specifically, the "green space of the base year" is the green space of the planning area in the base year, and the "green space of the optimization year" is the green space of the planning area in the optimization year.
According to the embodiment of the application, the net carbon dioxide absorption amount of the green space can be calculated through the carbon dioxide concentration change monitored by the carbon dioxide concentration observation station near the ground; the carbon dioxide absorption can also be calculated by using satellite remote sensing, meteorological observation data and the like and using corresponding calculation models, and specific calculation models and calculation processes can refer to related technologies, which are not described herein again and are all within the protection scope of the embodiment of the application.
As an alternative embodiment, a part or all of the green space region of the base year (hereinafter simply referred to as "selected region") may be selected to calculate the net carbon dioxide absorption amount thereof, from which the net carbon dioxide absorption amount per unit area of the green space is calculated, and it should be understood that the net carbon dioxide absorption amount of all of the green space region may be calculated from the net carbon dioxide absorption amount per unit area of the green space. For example, soil heterotrophic respiration R may be calculated by calculating annual soil respiration for the selected area and then using the annual soil respiration h And calculates the net primary productivity of the corresponding vegetation. The net carbon dioxide uptake in the selected area can be calculated based on the net primary productivity of the vegetation and the heterotrophic respiration of the soil.
Specifically, soil respiration can be calculated by formula 1, formula 1 being as follows:
… … type 1
Wherein R is S For annual soil respiration (kg Cm) -2 a -1 ) The method comprises the steps of carrying out a first treatment on the surface of the T is the annual average air temperature (DEG C); p is annual precipitation (m); SOC is soil carbon density (kg Cm) of surface soil (0-20 cm) -2 ) The system can be obtained through field monitoring, and can also be obtained from related data (such as Chinese second soil census data, related SOC literature data or a database and the like); q is an exponential relationship between soil respiration and temperature; k is a half-saturation constant of hyperbolic relation of soil respiration and annual precipitation, and can be 0.68; m is the half saturation constant of the hyperbolic relationship of soil respiration and soil carbon density. As a possible implementation, the parameter (R may be set up by establishing 0 Q, K, M) and the data set, calculating R 0 The values of Q, M, specific data set selection and nonlinear least squares fitting calculation may refer to the related art, and will not be described in detail herein.
Soil heterotrophic respiration can be calculated by the following formula 2, wherein R h Is the heterotrophic respiration value of the soil.
… … type 2
Wherein R is h Is the annual heterotrophic respiration value of the soil.
The net primary productivity of vegetation can be calculated using the CASA (Carnegie-Ames-Stanford approach, karman-emmes-stanford method) model. The CASA model is a model constructed based on the karman-emmes-stanford method, and is used for estimating the net primary productivity of the land ecosystem, and the process of calculating the net primary productivity of vegetation by using CASA can refer to the related technology, and will not be described in detail herein.
In the embodiment of the application, the net ecosystem productivity of the green space is equal to the net primary productivity of vegetation minus the heterotrophic respiration of soil. Illustratively, as shown in reference to equation 3, the net primary productivity of vegetation for 12 months (1 year) is poor from annual soil heterotrophic respiration, resulting in a net ecosystem productivity of green space (KgC/m 2 A) converting the net ecosystem productivity according to equation 4 to obtain a net carbon dioxide uptake. 3 and formula (III)4 is as follows:
… … type 3
Wherein NEP is net ecosystem productivity (KgC/m 2 ·a）；NPP i Net primary productivity of vegetation for month i in 1 year.
… … type 4
Wherein Sc is the annual net carbon dioxide uptake in the selected region (10 4 t) dividing Sc by a to obtain annual net absorption of carbon dioxide in unit area of the selected area; n is the number of grids divided by the sample area;NEP value for the q-th grid (KgC/m 2 ·a）；/>Is the area (m 2 ) The method comprises the steps of carrying out a first treatment on the surface of the 44/12 is CO 2 And molar mass ratio of C. It should be appreciated that the net carbon dioxide uptake calculations may be performed separately for different types of green space in selected areas of each type of green space.
According to the embodiment of the application, the influence of factors such as air temperature, precipitation, surface soil carbon density and the like on soil respiration can be comprehensively calculated by the formula 1, the obtained result is more objective and effective, the heterotrophic respiration of the soil can be conveniently calculated by the formula 2, the whole calculation process is not required to be complex, and the operation is simple and easy to implement.
In the embodiment of the application, the carbon dioxide emission amount of the planned area in the base period of the base period year can be calculated by respectively calculating the carbon dioxide emission amounts of all the fields, and specific calculation methods can refer to related technologies, for example, list compiling methods in the "IPCC 2006 national greenhouse gas list guide 2019 revision", or the carbon dioxide emission amount accounting methods provided by the data such as the "chinese petroleum and natural gas production enterprise greenhouse gas emission accounting method and reporting guide", the "chinese coal production enterprise greenhouse gas emission accounting method and reporting guide", the "chinese chemical production enterprise greenhouse gas emission accounting method and reporting guide", and the like, which are not described in detail herein, are all within the scope of the application.
In some alternative embodiments, calculating the basal carbon dioxide emissions of the planned region at the basal year includes: and calculating the energy consumption, industrial production and carbon dioxide emission of agricultural production of the planning area in the base period. That is, the carbon dioxide emission amount of the planned area in the base year can be obtained by calculating the carbon dioxide emission amounts of the planned area in the three fields of energy consumption, industrial production and agricultural production in the base year.
Specifically, in terms of energy consumption, raw coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, natural gas and 9 kinds of power consumption varieties are selected to calculate carbon dioxide generated by energy consumption, and a calculation formula is as follows:
… … type 5
Wherein C is p CO generated for energy consumption 2 Discharge (ten thousand tons); i is the i-th energy source in the 9 kinds of energy consumption varieties above; c (C) i For the consumption amount (ten thousand tons) of the energy i, ci can be obtained by referring to related statistical data, for example, the consumption amount of the energy in Beijing city can be obtained by referring to Beijing city statistical annual book; EF (electric F) i Carbon emission coefficient (tCO) as the i-th energy source 2 T) can be calculated by the following equation 6.
… … type 6
Wherein Vpc i Carbon content (tC/TJ) per unit heating value of the ith energy source, vmc i Average heating value of fuel (TJ/10) as the ith energy source 3 t）; R i Is the hydrocarbon rate of the ith energy source. Vpc i And Vmc i Can be obtained by referring to general rule of comprehensive energy consumption calculation, R i Reference may be made toProvincial greenhouse gas inventory guidelines.
Carbon dioxide emissions generated in industrial production such as steel production are mainly expressed in terms of energy consumption, so after energy consumption is removed, industrial production is mainly generated by cement production, so for simplifying operation, the carbon dioxide emissions in industrial production are calculated only for carbon dioxide emitted in cement production, and the calculation formula is as shown in formula 7:
… … type 7
Wherein C is q CO for cement production 2 Discharge amount; m is M c The cement yield is ten thousand tons; c (C) cl Taking 75% of cement clinker as a cement clinker proportion; EF (electric F) clc The carbon emission factor of the cement clinker can be 0.52 ten thousand tons of CO 2 Ten thousand tons of clinker.
In the aspect of agricultural production, carbon dioxide emission is mainly used for chemical fertilizers, pesticides, mulching films and machinery, and the chemical fertilizers mainly comprise nitrogen fertilizers, phosphate fertilizers, potash fertilizers and compound fertilizers. Because carbon dioxide emission generated in the using process of agricultural equipment is mainly embodied in the aspect of energy consumption, in order to avoid repeated statistics, the carbon dioxide emission in the using process of agricultural input factors such as chemical fertilizers, pesticides, mulching films and the like is mainly measured and calculated, and a calculation formula is shown as formula 8:
… … type 8
Wherein C is r Carbon dioxide emission (ten thousand tons) in the agricultural production process; e (E) i Carbon emission (ten thousand tons) for various agricultural input elements; t (T) i The consumption (ten thousand tons) of different agricultural input elements can be determined according to the sales of the different agricultural input elements, and can also be determined according to related investigation reports or statistical data, such as data of Chinese urban statistics annual book, chinese agricultural annual book and the like; c (C) i For the carbon emission coefficient of different agricultural input factors, the nitrogen fertilizer can be 12.44, the phosphate fertilizer can be 2.33, the potassium fertilizer can be 0.66, the compound fertilizer can be 1.40 and the pesticide can be used18.09 may be taken and 18.99 may be taken.
According to the embodiment of the application, the carbon dioxide emission of the planning area in the base period is obtained by calculating the energy consumption of the planning area in the base period, the carbon dioxide emission of the industrial production and the agricultural production, and the carbon dioxide emission of each field can be comprehensively counted, so that the calculation result is complete and effective. In addition, in the calculation process, carbon dioxide discharged in cement production is mainly calculated in the aspect of industrial production, carbon dioxide discharged in the use process of chemical fertilizers, pesticides and mulching films is mainly calculated in the aspect of agricultural production, and repeated carbon dioxide counted in the aspects of industrial and agricultural production, energy consumption and the like in steel production, agricultural appliance use and the like can be avoided, so that the calculation of the carbon dioxide discharge is more accurate.
S102: the carbon offset capacity of the green space of the base year is determined based on the net carbon dioxide uptake and the base carbon dioxide emissions.
From the net carbon dioxide uptake and the base carbon dioxide emissions, the carbon offset capacity of the green space in the base year can be calculated. The carbon offset capability may represent the absorption capability of the green space for carbon dioxide generated by the planned area. Specifically, the carbon offset capability of the green space of the basal year is equal to the ratio (percent) of the net carbon dioxide absorption of the green space of the basal year to the carbon dioxide emission of the planned area in the basal year.
S103: acquiring a plurality of candidate lifting targets with carbon offset capability, and predicting the predicted carbon dioxide emission of a planning area in an optimized year; and determining the number of candidate green spaces respectively corresponding to the plurality of candidate lifting targets according to the predicted carbon dioxide emission and the carbon offset capability.
The carbon counteracting capability may be a candidate lifting target automatically generated by the machine or a candidate lifting target manually set, which is not limited in this regard by the present application. Specifically, the plurality of candidate lifting targets may be set according to a certain gradient, for example, the plurality of candidate lifting targets are set according to a gradient of 5% respectively: 5%, 10%, 15%, etc. may be set in other manners, which are all within the scope of the embodiments of the present application. In addition, it should be understood that the number of green spaces in the embodiment of the present application refers to the area value of the green space.
In the embodiment of the application, the prediction of the carbon dioxide emission in the planning area in the optimization year can be performed by adopting the modes of data statistics, constructing a prediction model or directly using the existing model, and the specific prediction method can refer to the related technology and is not described in detail herein, and is within the protection scope of the embodiment of the application.
In addition, the number of candidate green spaces corresponding to the plurality of candidate lifting targets respectively may be calculated according to the carbon cancellation capability and the candidate lifting targets in the basal year, alternatively, in order to facilitate calculation, a forest land or other type of land may be set as a standard green space, the number of standard green spaces corresponding to the plurality of candidate lifting targets respectively may be calculated based on the predicted carbon dioxide emission amount in the optimized year, the net carbon dioxide absorption amount per unit area of the plurality of candidate lifting targets and the standard green space, and the number of corresponding standard green spaces may be used as the number of candidate green spaces, with specific calculation formula shown in the following formula 9. It should be appreciated that the number of standard green spaces may be converted to the number of other types of green spaces based on the ratio of the carbon counteracting capabilities of the standard green spaces to the other types of green spaces, provided that the same carbon counteracting effect is achieved.
… … type 9
Wherein Q is standard To optimize the number of annual standard green spaces (hectares); e is the total carbon dioxide emission (ten thousand tons) of the optimized year; gamma is a preset target for optimizing annual carbon offset, and is equal to the annual carbon offset multiplied by a candidate lifting target; g standard Net uptake of carbon dioxide per unit area (ten thousand tons) for standard green space, e.g., G when woodland is standard green space standard The net absorption of carbon dioxide in unit area of the forest land.
In an alternative embodiment, a carbon emission prediction model can be constructed according to social parameters of the optimization year, and the predicted carbon dioxide emission of the planning area in the optimization year is predicted, wherein the social parameters comprise population scale, people average GDP, town ratio, industrial structure, energy structure and energy intensity of the optimization year; the carbon emission prediction model can be seen in equation 10.
… … the number of the pins per unit area 10,
wherein, I IS the predicted carbon dioxide emission of the optimization year, P IS the population scale of the optimization year, A IS the average GDP of the optimization year, U IS the town ratio of the optimization year, IS IS the industrial structure of the optimization year, ES IS the energy structure of the optimization year, EI IS the energy intensity of the optimization year, the specific explanation of each variable IS shown in the following table 1, and each variable can be obtained through the related planning file of the country or the place.
In the carbon emission prediction model, a is a model coefficient, and b, c, d, f, g, h is a coefficient of a corresponding variable. The a-h can be obtained through regression or fitting analysis, for example, fitting by using least square method or ridge regression method, and specific fitting process can refer to related technology, which is not described herein, and is within the scope of the embodiments of the present application. In addition, the data used for regression or fitting may be obtained from related statistics documents, for example, the number of resident population, town rate, people's average GDP, and industry structure data of Beijing city statistics annual book, shanxi province statistics annual book, etc. in calendar years, and the energy structure and energy intensity data of each place may be obtained from Chinese energy statistics annual book, etc. in calendar years.
Table 1. Variable description table:
in the embodiment of the application, the carbon emission prediction model is constructed based on social parameters such as population scale, average person GDP, urban rate, industrial structure, energy intensity and the like in the optimal year, and the predicted carbon dioxide emission of the planning area in the optimal year can be predicted according to various factors such as population, economy, technology (industrial structure), energy and the like, so that the prediction result is more objective and accurate, and the effect of green space planning is improved.
S104: and determining the number of planned green spaces from the plurality of candidate green spaces according to the land constraint condition.
Land constraints are constraints set by the country or place for the use of various types of land, such as cultivated land red lines, woodland lower limits, etc. It should be understood that the "land type" in the present application includes not only land but also all land types of waters, construction lands and the like. According to the land constraint, the maximum number of candidate green spaces that satisfies the land constraint may be determined as the planned green space number, for example, the maximum number of candidate green spaces that satisfies the land constraint may be determined as the planned green space number. After the number of the planned green spaces is determined, the number of the green spaces of various land types in the optimized year can be divided specifically by maximizing ecological benefit or maximizing economic benefit, and other suitable modes can be adopted to divide the number of the planned green spaces, which are all within the protection scope of the embodiment of the application.
In some alternative embodiments, determining the number of planned green spaces from the number of candidate green spaces according to the land constraint includes:
and constructing an objective function by taking the sum of the economic benefit and the ecological benefit as a planning objective, and determining the number of the planned green spaces and the number of the corresponding land types from the plurality of candidate green spaces based on the land constraint conditions and the objective function.
Specifically, the objective function is shown in the following formula 11:
f (X) =max (a1×x1+a2×x2+ … +an×xn) … … formula 11
Wherein F (X) is an objective function, X1, X2 … Xn are areas of n land types in the region, and a1, a2 … an are average values of economic benefit and ecological benefit of unit area corresponding to the n land types; the number of planned green spaces and the corresponding number of individual land types are determined from the number of candidate green spaces based on the land constraint and the objective function.
Based on the land constraint condition and the objective function, the number of planned green spaces and the number of corresponding land types can be determined from the plurality of candidate green space numbers by calculating an optimal solution of the objective function.
In the embodiment of the application, the objective function is constructed by taking the sum of the economic benefit and the ecological benefit as the maximum planning target, so that the economic benefit and the ecological benefit can be considered, the economic and ecological requirements of urban planning are better met, and a better planning effect is realized.
The economic benefit per unit area can be calculated by the following steps.
(1) And counting the economic benefits of the n types of land according to the statistical annual-image data of the planning area, and determining the relative proportion of the economic output of each type of land.
(2) Calculating economic benefits of unit area of the annual farmland, and predicting and optimizing the economic benefits of the unit area of the annual farmland by using a gray prediction model. Wherein the gray prediction model (Gray Forecast Model) is a prediction model that can make predictions with small amounts of incomplete information. According to the embodiment of the application, related data can be acquired through planning the annual statistics annual bill or other literature data of the area, and the economic benefit of annual farmland unit area can be predicted and optimized according to the acquired data through the grey prediction model. Specifically, the embodiment of the application can use the existing grey prediction model; the gray prediction model can also be reconstructed, and the specific model construction process can refer to the related technology, and is not repeated herein, and is within the protection scope of the embodiment of the present application.
(3) And calculating the economic benefit value of the other types of land in unit area according to the relative proportion of the other types of land and the economic benefit of the cultivated land.
The evaluation criteria of the ecological benefits are different, and the calculation method is also various, so that the total ecological benefits are calculated after various ecological benefits are quantized, and specific quantization criteria and calculation processes can refer to related technologies and are not repeated here.
As an alternative embodiment, the ecological benefits per unit area can be calculated by the following steps.
(1) Standard equivalent values (also called ecosystem service value amounts) of 1 standard equivalent factor corresponding to a plurality of historical years are calculated.
D=sr×fr+sw×fw+sc×fc … … formula 12
Wherein: d represents a standard equivalent value, i.e., an ecosystem service value (Yuan/hm 2 Hm is hundred meters); sr, sw and Sc respectively represent the percentage (%) of the sowing area of rice, wheat and corn in a certain historical year to the total sowing area of three crops, and Fr, fw and Fc respectively represent the average net profit per unit area (Yuan/hm) of rice, wheat and corn in the whole country in the historical year 2 ). The sowing area can be referred to the statistical annual-differentiation data of the planning area, and the average net profit of the unit area of the grain can be referred to the related data of national agricultural product cost benefit data assembly.
(2) And predicting the standard equivalent value of the optimization year based on the gray prediction model.
Inputting standard equivalent calculated for a plurality of historical years into a gray prediction model, and predicting standard equivalent value (yuan/hm of the optimized year 2 ). Similar to the prediction economic benefit, the gray prediction model can also be an existing gray prediction model, or a gray prediction model can be reconstructed, and specific prediction processes can refer to related technologies, which are not described herein.
(3) The standard equivalent value of the optimized year is multiplied by the service value equivalent of the ecological system in unit area of the n types of land, and the obtained product can be respectively used as the ecological benefit value (yuan/hm) in unit area corresponding to various lands 2 ). In some alternative embodiments, the ecosystem service value of the land may be divided into four parts of values of a supply service, a regulation service, a support service and a culture service, that is, the ecosystem service value equivalent is the sum of the values of the supply service, the regulation service, the support service and the culture service, and the ecosystem service value equivalent per unit area of each type of land may be calculated according to the following table 2.
Table 2. Per unit area ecosystem service value table:
in the embodiment of the application, the standard equivalent value is calculated according to the related data such as the average net profits of three main grains of rice, wheat and corn, and the used data is simple and easy to obtain, so that the workload of data acquisition can be reduced. And the ecological benefit is calculated by adopting the predicted standard equivalent value of the optimized year, so that the error caused by the change of the year can be reduced, and a more accurate calculation result can be obtained. In addition, in the embodiment of the application, the ecological benefit is calculated by multiplying the standard equivalent value by the equivalent serving value of the ecological system in unit area of the land, and the standard equivalent value is calculated based on the average net profits of three main grains of rice, wheat and corn, so that part of economic factors are added in the process of calculating the ecological benefit, which is beneficial to quantifying the ecological benefit, so that the calculation of the ecological benefit is more scientific and reasonable.
In some alternative embodiments, the land constraint conditions include a land conservation constraint, a woodland constraint, a water area constraint, a urban and rural construction land constraint, an ecological space constraint, a land utilization total area constraint, and the like, and each constraint condition can be determined according to a relevant planning file, for example, beijing city overall planning (2016-2035), beijing city domestic space recent planning (2021-2025), and the like.
It should be appreciated that the number of candidate green spaces may be used as a constraint condition in addition to the land constraint to determine the number of planned green spaces from the number of candidate green spaces; in order to ensure that the number of green spaces of various land types is not negative, the number of green spaces of various land types may be 0 or more as a constraint condition.
In the embodiment of the present application, a Multi-Objective-Programming (MOP) model can be constructed based on a land constraint condition and an Objective function to calculate an optimal solution of the Objective function under the constraint condition so as to obtain the number of planned green spaces, and it should be understood that the obtained planned green space number result includes the numbers corresponding to the green spaces of various land types, respectively.
The multi-objective planning model is an analysis decision model which combines qualitative analysis and quantitative analysis by analyzing multi-factor constraint and multi-objective combination. The multi-objective planning model comprises an objective function and constraint conditions, and in the embodiment of the application, the constraint conditions can be land constraint conditions; as for the objective function, other objective functions may be provided in addition to the above objective function 11, for example, an objective function may be constructed with the lowest planning cost or land improvement cost as an objective. The specific construction and operation process of the multi-objective planning model can refer to the related technology, and are not described herein, and are all within the protection scope of the embodiments of the present application.
S105: and determining candidate distribution areas of the green space of the optimized year according to the position of the green space of the basal year.
According to the position of the green space in the base period, the candidate distribution area in the green space in the optimized year may be determined, for example, the position of the green space in the base period and the area around the position may be determined as the candidate distribution area in the green space in the optimized year, or the position of the green space in the base period and the unused area in the planning area may be determined as the candidate distribution area in the green space in the optimized year, or the like, and of course, other suitable manners may be adopted to determine the candidate distribution area in the green space in the optimized year, which is within the scope of the embodiment of the present application. In addition, it should be appreciated that the candidate distribution area is located within the planning area.
In some alternative embodiments, determining candidate distribution areas for green space for an optimal year based on the location of green space for the base year comprises: determining a resistance distribution map of the planning area by carrying out weighted summation on ventilation resistance and biological migration resistance of the planning area, wherein the weight values of the ventilation resistance and the biological migration resistance are respectively 0.3 and 0.7; candidate distribution areas of the green space of the optimal year are then determined based on the resistance profile and the location of the green space of the baseline year.
The ventilation resistance refers to the resistance of the building/terrain to wind flow during air flow, the biological migration resistance refers to the resistance of the building/terrain/vegetation cover/road and the like to migration of various organisms, and the ventilation resistance and the biological migration resistance can be represented in a grid chart by equal resistance lines or different gradient colors.
In the embodiment of the application, the ventilation resistance can be expressed by using an index of windward area density (Frontal Area Density, FAD), and the calculation formula is as follows.
… … type 13
Wherein lambda is f(z,θ) The wind-facing surface density index corresponding to the theta wind direction, namely the ventilation resistance of the theta wind direction, obtains wind rose diagrams of a planning area by using meteorological data, calculates ventilation resistance in multiple directions, and performs weighted average according to wind frequencies in multiple directions to obtain the ventilation resistance of the planning area; a is that F(θ) Is the projected area of the building/terrain perpendicular to the theta wind direction; a is that T Is the area of the grid cell; θ is a specific wind direction; z is Z meanT Is the average height of the grid cells.
As a possible implementation manner, the biological migration resistance can be calculated according to the reciprocal of the habitat quality, the night light index and the topography index, and the calculation formula is as follows.
… … type 14
Wherein R is z Representing the biological migration resistance value of the unit grid of the research area; r is R 0 The reciprocal of the habitat quality, which is used to represent the ability of the ecosystem to provide suitable conditions for the continued survival and development of individuals and populations, can be calculated by analyzing land utilization and land utilization/land coverage maps and their threat levels to biodiversity, and specific analysis methods can be found in the related art and are not described in detail herein; OLS is night light index, which can be obtained through remote sensing images, and can also directly refer to data published by each large remote sensing information website; s is S per Expressed as a percentage grade for a terrain index, may be based on a terrain map with elevation dataObtained.
After the ventilation resistance and the biological migration resistance are obtained, a grid image processing program such as a grid calculator can be used for carrying out weighted summation on the ventilation resistance and the biological migration resistance of the planning area so as to determine a resistance distribution diagram (grid diagram) of the planning area, wherein the weighted values of the ventilation resistance and the biological migration resistance can be respectively 0.3 and 0.7.
In the embodiment of the application, a green space with a annual base area larger than 1 square kilometer is used as a source plaque to obtain a source plaque file (shp format vector file), and the source plaque file obtaining process can refer to the related technology and is not described in detail herein, and the method is within the protection scope of the embodiment of the application.
And inputting the source plaque file and the resistance distribution diagram into a circuit model based on a circuit theory for analysis, obtaining the accumulated current density between the source plaque through iterative calculation, and taking the region with the current density value ranked at the top 50% as a candidate distribution region for setting a green space in an optimization year to obtain a candidate distribution region grid diagram. The circuit theory is to simulate the migration and diffusion process of living beings in the environment by using the random walk characteristic of electrons in the circuit, wherein the source plaque is used as a circuit node, the resistance between the source plaque is used as a resistor, the current between the source floor blocks is calculated, and the higher the current density, the more frequent the biological migration of the region is. The specific circuit model and the related operation process can refer to the related technology, and are not described herein again, and are all within the protection scope of the present application.
According to the embodiment of the application, the resistance distribution diagram of the planning area is constructed based on the ventilation resistance and the biological migration resistance, and the candidate distribution area of the green space in the optimized year is determined, so that the ventilation effect of the green space can be effectively improved, the carbon dioxide absorption capacity of the green space is improved, the carbon sink effect of the green space is enhanced, the biological migration among the green areas can be ensured as much as possible, the biological diversity is protected, and the ecological benefit of the green space is improved.
S106: and planning the positions and the number of the green spaces in the optimized year according to the number of the green spaces and the candidate distribution areas.
In the embodiment of the application, the number of planned green spaces is used as the number of green spaces arranged in an optimized year, the elevation, the gradient, the annual average air temperature and the like are selected as climate environment driving factors, and the kilometer grid GDP, the population density, the distance to a railway, the distance to a highway and the like are selected as socioeconomic driving factors. Extracting expansion parts of various lands in the two-stage historical land utilization map, excavating factors of various land utilization expansion and driving force by adopting methods such as a random forest algorithm and the like, and acquiring the development probability of various lands and the contribution of the driving factors to various land expansion in the two-stage historical land utilization map. The random forest algorithm is an existing algorithm which combines a plurality of classifiers and votes or averages the results of the classifiers, so that the results of the overall model have higher accuracy and generalization performance, and the process of mining the factors of expansion and driving force of various land utilization by adopting the random forest algorithm can refer to the related technology and is not repeated herein. The two-stage historical land utilization map should be at certain intervals, for example, 10 years, 15 years, 20 years, etc. each corresponding to a year.
After the development probability of various lands and the contribution of driving factors to the expansion of various lands in the period are obtained, the development probability of the green space is adjusted by combining the candidate distribution areas, so that the development probability is higher in the areas with higher current density, and the weight of the development probability of the green space in the candidate areas can be increased according to the current density of the candidate areas. And arranging green areas for planning the number of green spaces in the planning area according to the adjusted development probability and the contribution of various driving factors to various land expansions.
As a possible implementation, a suitable CA (Cellular Automata, cellular automaton) model may be used to spatially position the green areas for planning the number of green spaces, e.g. a CA model based on a plurality of classes of random plaque seeds, etc. The CA model is a grid dynamics model with discrete time, space and state, and the space interaction and time causal relationship are local, so that the factors such as the development rule of the land in a certain period of time, the space position characteristics of the land and the like can be utilized, and the expansion process of various lands can be simulated according to the regulated development probability and the contribution of various driving factors to the expansion of various lands, so that the arrangement of various lands included in the green space is realized; CA model based on multiple kinds of random plaque seeds is a model for simulating land utilization change, which can generate change seeds according to neighborhood effects, and based on the adjusted development probability and contribution of various driving factors to expansion of various lands, the development of the seeds is adjusted to simulate the expansion process of various lands, so that arrangement of various lands comprising green space is realized, wherein the neighborhood effects refer to interactive attraction and repulsion of local land utilization, are expression of local influence, and neighborhood effect calculation can refer to related technologies and are not repeated herein.
In the above process, the CA model may call the CA model in the existing land analysis program, or may additionally construct a new CA model, and specific CA model selection application or construction process may refer to related technologies, which are not described herein.
For ease of understanding by the skilled artisan, the overall flow of green space planning in an alternative embodiment of the application is schematically illustrated with reference to fig. 2 of the specification.
As shown in fig. 2, in the embodiment of the present application, the net carbon dioxide absorption of the green space in the planned area of the basal year is calculated according to the net primary productivity of vegetation and heterotrophic respiration of soil. And obtaining the emission of the carbon dioxide in the basal period of the planning area in the basal period by calculating the emission of the carbon dioxide in the three fields of energy consumption, industrial production and agricultural production of the planning area in the basal period. And then calculating the carbon offset capacity of the green space in the base year according to the obtained net carbon dioxide absorption and carbon dioxide emission. Setting a plurality of candidate lifting targets for carbon offset capability according to a gradient of 5% is: 5%, 10%, 15%, etc., and calculating carbon offset capacities corresponding to the plurality of candidate lifting targets.
And predicting the predicted carbon dioxide emission of the planning area in the optimized year according to the social parameters such as population scale, people average GDP, town rate, industrial structure, energy intensity and the like in the optimized year. And calculating the number of the standard green spaces respectively corresponding to the candidate lifting targets based on the predicted carbon dioxide emission amount of the optimal year, the carbon cancellation capacity corresponding to the candidate lifting targets and the net carbon dioxide absorption amount per unit area of the standard green spaces.
And then setting the areas of n land types in planning areas such as X1, X2, … Xn and the like as variables, setting land constraint conditions and constructing an objective function, and analyzing and solving the objective function according to the land constraint conditions to obtain the number of the planned green spaces.
Based on the ventilation resistance and the biological migration resistance of the planned area, candidate distribution areas for setting a green space in the optimal year are determined.
And finally, comprehensively planning the positions and the number of the green spaces in the optimized year according to the number of the planned green spaces and the candidate distribution areas to obtain a green space planning diagram in the optimized year.
According to the embodiment of the application, the number of the planned green spaces is determined from the number of the candidate green spaces according to the land constraint condition, the candidate distribution area for setting the green spaces in the optimized year is determined according to the positions of the green spaces in the base period, and the number configuration and the space layout are combined, so that the positions and the number of the green spaces in the optimized year can be scientifically and effectively planned, the carbon sink capacity of the green spaces is enhanced, and the requirement of urban green space planning is better met. In addition, the embodiment of the application can clearly optimize the number and the space position of the green space of the year by planning the position and the number of the green space of the year, and the urban ecological planning and construction of the year of the period of the foundation and the year of the optimization are carried out so as to improve the feasibility of the low-carbon development of the city.
In order to better explain the technical scheme of the application, the green space planning process in 2035 of Beijing city is exemplarily described below with 2020 as the base year and 2035 as the optimization year.
The total carbon dioxide net absorption amount and the carbon dioxide net absorption amount of 4 types of green spaces of the woodland, the grassland, the cultivated land and the unutilized land are calculated based on the CASA model and the soil respiration model, the woodland is used as a standard green space, the coefficient for converting the grassland, the cultivated land and the unutilized land into the woodland according to the carbon dioxide absorption capacity, namely the coefficient for folding woodland, the carbon dioxide net absorption amount and the coefficient for folding woodland of the unit area of different green spaces in the 2020 year of Beijing city are shown in the table 3, wherein the carbon dioxide net absorption amount of the unit area is the carbon dioxide net absorption amount of the green space per hectare, and the coefficient for folding woodland can be obtained according to the ratio of the carbon dioxide net absorption amount of the cultivated land, the grassland, the unutilized land and the woodland.
Table 3. Net carbon dioxide uptake per unit area for different green spaces in beijing, 2020:
the energy consumption, industrial production and agricultural production of the planning area in the base year are calculated, the carbon dioxide emission in the three areas is added to obtain the total carbon dioxide emission in the planning area, the specific calculation method can refer to the embodiment above, details are omitted here, and the calculation result is shown in fig. 3.
Calculating the ratio (percentage) of the total carbon dioxide net absorption amount to the total carbon dioxide emission amount of all green spaces in the planning area, wherein the carbon offset capability of the green space 2020 in Beijing city is 3.74%, the candidate lifting targets of the carbon offset capability of the green space in 2035 are preset according to a lifting gradient of 5%, and the carbon offset capability of the green space in 2035 is required to be lifted to 3.93% (lifting 5%), 4.11% (lifting 10%), 4.30% (lifting 15%), 4.49 (lifting 20%) or 4.68% (lifting 25%) according to the candidate lifting targets.
According to the historical data from 2005 to 2020, a ridge regression method is adopted to carry out fitting calculation on the formula 10, and the formula 10 is obtained as follows:。
wherein, in the fitting process, the town ratio, the industrial structure and the energy structure are threeThe individual factors have multiple collinearity and fail to pass the significance test of 5%, so the coefficients d, f and g of the town ratio, the industrial structure and the energy structure are all 0. It is expected that the population size (P) in 2035 will be controlled to be about 2300 ten thousand people, and the average person GDP (A) is 32 ten thousand yuan; energy Intensity (EI) was expected to be 0.1246 in 2035. The STIRPAT model is adopted to predict that the urban carbon dioxide emission of 2035 in Beijing city is about 26540 ten thousand tons; according to the total carbon dioxide carbon emission of the 2020 planning area and the carbon offset capability (namely 3.93%, 4.11%, 4.30%, 4.49 or 4.68%) corresponding to the 2035 carbon offset capability candidate lifting target, the standard green space quantity corresponding to the 2035 different carbon offset capability candidate lifting targets respectively is measured and calculated to be 12208.30km 2 、12767.45 km 2 、13357.68 km 2 、13947.90 km 2 And 14538 km 2 。
In the embodiment of the application, the planning area is divided into 6 land types of cultivated land, woodland, grassland, water area, construction land and unused land, and the cultivated land area X1, the woodland area X2, the grassland area X3, the water area X4, the construction land area X5 and the unused land area X6 are used as variables of an objective function. Based on the number of standard green spaces corresponding to different carbon cancellation capability candidate lifting targets, the land constraint conditions are set according to the following table 4 in combination with the planning requirements of Beijing urban general planning (2016-2035), beijing urban domestic soil space recent planning (2021-2025) and land utilization change conditions.
Table 4. Land constraint:
the economic benefit of the unit area of the 2035-year cultivated land is 125961 yuan/ha, the woodland is 35527 yuan/ha, the grassland is 122731 yuan/ha, the water area is 9689 yuan/ha, the construction land is 15854936 yuan/ha, and the unused land is 0 yuan/ha. The grey model is used for predicting 15625 yuan/ha of the unit area of the cultivated land of 2035, 88213 yuan/ha of the woodland, 76255 yuan/ha of the grassland, 412052 yuan/ha of the water area, 0 yuan/ha of the construction land and 764 yuan/ha of the unutilized land.
The maximum planning target of the sum of economic benefit and ecological benefit of 2035 years in Beijing city is as follows:
F (X) =max (70793x1+61870x2+99493x3+210871x4+79274638 x5+382x6) … … type 15
Then using the multi-objective planning model to calculate the optimal solution of the objective function under the constraint of the constraint condition of Table 4, the cultivated area X1 is 1106.66 km 2 Forest land area X2 is 11895.42 km 2 Grassland area X3 is 277.41 km 2 Area X4 of water is 370.51 km 2 The construction area X5 is 2760 km 2 Unused area X6 is 0 km 2 。
And respectively calculating the ventilation resistance and the biological migration resistance of the planning area by using the formulas 13 and 14, carrying out weighted summation on the ventilation resistance and the biological migration resistance of the planning area, determining a resistance distribution diagram of the planning area, respectively taking weight values of the ventilation resistance and the biological migration resistance to be 0.3 and 0.7, and calculating the ventilation resistance, the biological migration resistance and the resistance distribution diagram of the planning area, wherein different color gradients are adopted to represent different resistance values. The topography in the planning area mainly comprises mountain areas, suburban areas and central urban areas, a green space with the area larger than 1 square kilometer in the planning area is selected as a source land, and the accumulated current density between the plaques is calculated by iteration of a circuit model, so that an accumulated current density distribution diagram is obtained, and is shown in fig. 4D. Referring to fig. 4D, the region with the top 50% of the current density value rank is selected as the candidate distribution region set in the 2035 green space.
And 5 climate environment driving factors including elevation, gradient, annual average air temperature, annual average precipitation and distance to a water area are selected, and 8 socioeconomic driving factors including kilometer grid GDP, population density, distance to a railway, distance to a highway, distance to a provincial road, distance to a national road, distance to a county road and distance to other urban roads are selected. Based on the land utilization diagrams in 2005 and 2020 2, the expansion parts of various land used in the land change in 2005-2020 are extracted, the development probability of each land utilization type and the contribution degree of each driving factor to the land type change and expansion are analyzed, and the development probability of the land of the type is adjusted by combining with the candidate distribution area.
And according to the adjusted development probability and the contributions of various driving factors to the expansion of various lands, the existing CA model based on the various random plaque seeds is called to arrange the positions of various lands in a planning area according to the areas of various lands obtained by the multi-objective planning model, and finally, a green space planning diagram shown in figure 5 is obtained.
As shown in fig. 6, an embodiment of the present application provides a green space planning system for increasing carbon sink, which includes a carbon dioxide calculating unit 601, a carbon offset capability calculating unit 602, a candidate number determining unit 603, a planning number determining unit 604, a candidate distribution area determining unit 605, and a green space planning unit 606.
The carbon dioxide calculation unit 601 is configured to calculate a net carbon dioxide intake of a green space of a base year and a base carbon dioxide emission of a planned area in the base year.
The carbon offset capability calculation unit 602 is configured to determine the carbon offset capability of the green space of the base year based on the net carbon dioxide absorption and the base carbon dioxide emission.
The candidate number determining unit 603 is configured to obtain a plurality of candidate lifting targets of carbon offset capability, and predict a predicted carbon dioxide emission amount of the planned area in the optimization year; and determining the number of candidate green spaces respectively corresponding to the plurality of candidate lifting targets according to the predicted carbon dioxide emission and the carbon offset capability.
The planned number determining unit 604 is configured to determine the planned number of green spaces from the plurality of candidate green spaces according to the land constraint condition.
The candidate distribution area determination unit 605 is configured to determine a candidate distribution area of the green space of the optimum year from the position of the green space of the base year.
The green space planning unit 606 is configured to plan the position and the number of green spaces in the optimization year according to the number of planned green spaces and the candidate distribution area.
The green space of the basal period year is the green space of the planning area in the basal period year, and the green space of the optimization year is the green space of the planning area in the optimization year.
The green space planning system for increasing carbon sink and the above green space planning method embodiment for increasing carbon sink provided by the embodiment of the present application can achieve the same effect based on the same inventive concept, and the specific implementation process can be referred to the description in the foregoing green space planning method embodiment, and will not be described herein.
Referring to fig. 7, a schematic structural diagram of an electronic device according to another embodiment of the present application is shown, and the specific embodiment of the present application is not limited to the specific implementation of the electronic device.
As shown in fig. 7, the electronic device may include: a processor 702, a communication interface (Communications Interface), a memory 706, and a communication bus 708.
Wherein:
processor 702, communication interface 704, and memory 706 perform communication with each other via a communication bus 708.
Communication interface 704 for communicating with other electronic devices or servers.
The processor 702 is configured to execute the program 710, and may specifically perform relevant steps in the above-described embodiment of the green space planning method.
In particular, program 710 may include program code including computer-operating instructions.
The processor 702 may be a CPU or specific integrated circuit ASIC (Application Specific Integrated Circuit) or one or more integrated circuits configured to implement embodiments of the present application. The one or more processors comprised by the smart device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 706 for storing programs 710. The memory 706 may comprise high-speed RAM memory or may further comprise non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 710 may include a plurality of computer instructions, and the program 710 may specifically enable the processor 702 to perform the operations corresponding to the green space planning method for increasing carbon sink described in any one of the foregoing method embodiments.
The specific implementation of each step in the program 710 may refer to the corresponding steps and corresponding descriptions in the units in the above method embodiments, and have corresponding beneficial effects, which are not described herein. It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and modules described above may refer to corresponding procedure descriptions in the foregoing method embodiments, which are not repeated herein.
The present application also provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method described in any of the preceding method embodiments. The computer storage media includes, but is not limited to: a compact disk read Only (Compact Disc Read-Only Memory, CD-ROM), random access Memory (Random Access Memory, RAM), floppy disk, hard disk, magneto-optical disk, or the like.
Embodiments of the present application also provide a computer program product comprising computer instructions that instruct a computing device to perform operations corresponding to the green space planning method for increasing carbon sink in any of the above-described method embodiments.
In addition, it should be noted that, the information related to the user (including, but not limited to, user equipment information, user personal information, etc.) and the data related to the embodiment of the present application (including, but not limited to, operation data for execution, stored data, presented data, etc.) are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and a corresponding operation entry is provided for the user to select authorization or rejection.
It should be noted that, according to implementation requirements, each component/step described in the embodiments of the present application may be split into more components/steps, or two or more components/steps or part of operations of the components/steps may be combined into new components/steps, so as to achieve the objects of the embodiments of the present application.
The methods according to embodiments of the present application described above may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and to be stored in a local recording medium downloaded through a network, so that the methods described herein may be processed by such software on a recording medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware such as an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or field programmable or gate array (Field Programmable Gate Array, FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a Memory component (e.g., random access Memory (Random Access Memory, RAM), read-Only Memory (ROM), flash Memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor, or hardware, performs the methods described herein. Furthermore, when a general purpose computer accesses code for implementing the methods illustrated herein, execution of the code converts the general purpose computer into a special purpose computer for performing the methods illustrated herein.
Those of ordinary skill in the art will appreciate that the elements and method steps of the examples described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or as a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the embodiments of the present application.
The above embodiments are only for illustrating the embodiments of the present application, but not for limiting the embodiments of the present application, and various changes and modifications may be made by one skilled in the relevant art without departing from the spirit and scope of the embodiments of the present application, so that all equivalent technical solutions also fall within the scope of the embodiments of the present application, and the scope of the embodiments of the present application should be defined by the claims.

Claims (9)

1. A green space planning method for increasing carbon sink, comprising:
calculating the net carbon dioxide absorption of the green space of the basal period year and planning the basal period carbon dioxide emission of the area in the basal period year;
Determining a carbon offset capacity of a green space of a basal year based on the net carbon dioxide uptake and the basal carbon dioxide emission, wherein the carbon offset capacity of a green space of a basal year is a ratio of the net carbon dioxide uptake of a green space of a basal year to the basal carbon dioxide emission of a planned area in a basal year;
acquiring a plurality of candidate lifting targets of the carbon cancellation capability; predicting the predicted carbon dioxide emission of the planning area in the optimized year; determining the number of candidate green spaces respectively corresponding to the plurality of candidate lifting targets according to the predicted carbon dioxide emission and the carbon offset capability;
determining the number of planned green spaces from the plurality of candidate green spaces according to a land constraint condition, wherein the land constraint condition comprises carbon constraint, and the carbon constraint is that the number of planned green spaces is larger than or equal to the number of candidate green spaces corresponding to the candidate lifting targets of carbon cancellation capability;
according to the position of the green space in the basal period, determining candidate distribution areas of the green space in the optimized year;
according to the number of the planned green spaces and the candidate distribution areas, planning the positions and the number of the green spaces in the optimized year;
Wherein: the green space of the basal period year is the green space of the planning area in the basal period year, and the green space of the optimization year is the green space of the planning area in the optimization year;
the determining the candidate distribution area of the green space of the optimized year according to the position of the green space of the basal year comprises the following steps:
determining a resistance distribution map of the planning area by carrying out weighted summation on ventilation resistance and biological migration resistance of the planning area, wherein the weight values of the ventilation resistance and the biological migration resistance are respectively 0.3 and 0.7;
taking a green space with the annual area of the basal period being more than 1 square kilometer as a source land patch, and obtaining a source land patch file;
inputting the source plaque file and the resistance distribution diagram into a circuit model based on a circuit theory for analysis to obtain accumulated current density between the source plaque;
taking the area of which the current density value is ranked as the first 50% as the candidate distribution area for setting a green space in the optimal year; and, in addition, the processing unit,
and the step of planning the positions and the numbers of the green spaces in the optimized year according to the number of the green spaces and the candidate distribution areas comprises the following steps:
extracting expansion parts of various lands based on two-period historical land utilization diagrams, excavating various land utilization expansion and driving factors, and acquiring development probabilities of various lands and contributions of various preset driving factors to various land expansion;
According to the current density corresponding to the candidate distribution area, adjusting the development probability so that the higher the current density is, the higher the development probability is;
and arranging the green spaces of the planned green space quantity in a planning area according to the adjusted development probability and the contributions of the various preset driving factors to various land expansions.
2. The method of claim 1, wherein the calculating the net carbon dioxide absorption of the green space for the base year comprises:
calculating annual soil respiration of a green space of a basal year, and calculating heterotrophic soil respiration according to the annual soil respiration;
calculating the vegetation net primary productivity of the green space in the basal year, and determining the carbon dioxide net absorption amount according to the difference value of the vegetation net primary productivity and the heterotrophic respiration of the soil; wherein, the liquid crystal display device comprises a liquid crystal display device,
the annual soil respiration is calculated by the following formula:
，
wherein R is S For the annual soil respiration, the unit is kg Cm -2 a -1 The method comprises the steps of carrying out a first treatment on the surface of the T is the annual average air temperature in DEG C; p is annual precipitation, and the unit is m; soil carbon density of surface soil with SOC of 0-20cm, unit is kg Cm -2 The method comprises the steps of carrying out a first treatment on the surface of the Q is an exponential relationship between soil respiration and temperature; k is the half saturation constant of the hyperbolic relationship of soil respiration and annual precipitation; m is the half saturation constant of the hyperbolic relation of soil respiration and soil carbon density;
The soil heterotrophic respiration is calculated by the formula:
，
wherein R is h Is the heterotrophic respiration value of the soil.
3. The method of claim 1, wherein the determining the number of planned green spaces from the number of candidate green spaces according to a land constraint comprises:
taking the maximum sum of economic benefit and ecological benefit as a planning target, constructing an objective function as follows:
F（X）=max(a1*X1+a2*X2+…+an*Xn)，
wherein F (X) is an objective function, X1, X2 … Xn are areas of n land types in the planning area respectively, and a1, a2 … an are average values of economic benefits and ecological benefits of unit areas corresponding to the n land types respectively;
and determining the planned green space quantity and the corresponding quantity of each land type from a plurality of candidate green space quantities based on the land constraint condition and the objective function.
4. A method according to claim 3, wherein the ecological benefit is calculated by:
standard equivalents of 1 standard equivalent factor corresponding to a plurality of historical years are calculated, and the calculation formula is as follows:
D=Sr×Fr+Sw×Fw+Sc×Fc
wherein D represents the standard equivalent value, namely the ecosystem service value of 1 standard equivalent factor; sr, sw and Sc respectively represent the percentage of the sowing area of rice, wheat and corn in a certain historical year to the total sowing area of three crops, and Fr, fw and Fc respectively represent the average net profit per unit area of the rice, wheat and corn in the whole historical year;
Predicting a standard equivalent value of the optimization year based on a gray prediction model;
and multiplying the standard equivalent value of the optimized year by the service value equivalent of the ecological system in unit area corresponding to the n land types, and obtaining products which are respectively used as the ecological benefits in unit area corresponding to each land type.
5. The method of claim 1, wherein the predicting the predicted carbon dioxide emissions of the planned region at the optimal year comprises:
constructing a carbon emission prediction model according to social parameters of the optimized year, and predicting the predicted carbon dioxide emission, wherein the social parameters comprise population scale, people average GDP, town rate, industrial structure, energy structure and energy intensity of the optimized year; and, in addition, the processing unit,
the carbon emission prediction model is as follows:
，
i IS the predicted carbon dioxide emission, P IS the population scale, A IS the average GDP, U IS the town ratio, IS IS the industrial structure, ES IS the energy structure, EI IS the energy intensity, a IS a model coefficient, and b, c, d, f, g, h respectively represent coefficients of corresponding variables.
6. The method of claim 1, wherein the calculating the projected area's basal carbon dioxide emissions at the basal year comprises:
And calculating the energy consumption, industrial production and carbon dioxide emission of agricultural production of the planning area in the base period.
7. A green space planning system for increasing carbon sequestration, comprising:
a carbon dioxide calculation unit for calculating a net carbon dioxide absorption amount of a green space of the base year and a base carbon dioxide emission amount of the planned area in the base year;
a carbon offset capability calculation unit configured to determine a carbon offset capability of a green space of a base year based on the net carbon dioxide absorption amount and the base carbon dioxide emission amount, wherein the carbon offset capability of the green space of the base year is a ratio of the net carbon dioxide absorption amount of the green space of the base year to the base carbon dioxide emission amount of a planned area in the base year;
a candidate number determination unit configured to acquire a plurality of candidate lifting targets of the carbon offset capability; predicting the predicted carbon dioxide emission of the planning area in the optimized year; determining the number of candidate green spaces respectively corresponding to the plurality of candidate lifting targets according to the predicted carbon dioxide emission and the carbon offset capability;
a planned number determining unit, configured to determine a planned number of green spaces from the plurality of candidate green spaces according to a land constraint condition, where the land constraint condition includes a carbon constraint, and the carbon constraint is that the planned number of green spaces is greater than or equal to the number of candidate green spaces corresponding to the carbon cancellation capability candidate lifting target;
A candidate distribution area determining unit for determining a candidate distribution area of the green space of the optimized year according to the position of the green space of the base year;
the green space planning unit is used for planning the positions and the number of the green spaces in the optimized year according to the number of the planned green spaces and the candidate distribution areas;
wherein: the green space of the basal period year is the green space of the planning area in the basal period year, and the green space of the optimization year is the green space of the planning area in the optimization year;
the candidate distribution area determining unit is specifically configured to:
determining a resistance distribution map of the planning area by carrying out weighted summation on ventilation resistance and biological migration resistance of the planning area, wherein the weight values of the ventilation resistance and the biological migration resistance are respectively 0.3 and 0.7;
taking a green space with the annual area of the basal period being more than 1 square kilometer as a source land patch, and obtaining a source land patch file;
inputting the source plaque file and the resistance distribution diagram into a circuit model based on a circuit theory for analysis to obtain accumulated current density between the source plaque;
taking the area with the current density value ranked as the first 50% as the candidate distribution area for setting a green space in the optimal year; and, in addition, the processing unit,
The green space planning unit is specifically configured to:
extracting expansion parts of various lands based on two-period historical land utilization diagrams, excavating various land utilization expansion and driving factors, and acquiring development probabilities of various lands and contributions of various preset driving factors to various land expansion;
according to the current density corresponding to the candidate distribution area, the development probability is adjusted so that the higher the current density is, the higher the development probability is;
and arranging the green spaces of the planned green space quantity in a planning area according to the adjusted development probability and the contributions of the various preset driving factors to various land expansions.
8. An electronic device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the method of any one of claims 1-6.
9. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-6.
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