Drainage basin land utilization carbon source sink assessment method

Abstract

The invention relates to the technical field of environmental protection and land resource management, and discloses a drainage basin land utilization carbon source and sink evaluation method, which comprises the following steps: S1, constructing a land utilization classification and grading system; s2, acquiring carbon source and carbon sink driving data of various land utilization types based on the multi-source data; s3, according to different land utilization types, adopting corresponding carbon source and carbon sink accounting models to carry out carbon amount calculation; s4, simulating and predicting drainage basin land utilization carbon source and sink, selecting driving factors, setting four scenes of natural development, cultivated land protection, economic development and low-carbon guidance, applying a PLUS model, inputting historical land utilization data and driving factor data, setting model parameters, and simulating future drainage basin land utilization conditions; and S5, in combination with the accounting methods of S4 and S3, outputting a drainage basin land utilization carbon source and sink evaluation result. According to the method, the high-carbon sink area and the high-carbon emission area can be identified, and support is provided for drainage basin ecological protection red line delimiting and ecological restoration project layout.

Description

Technical Field

[0001] This invention relates to the field of environmental protection and land resource management technology, specifically to a method for assessing carbon sources and sinks in watershed land use. Background Technology

[0002] Watershed land use change directly affects the distribution of carbon sources and sinks. Land transfer processes such as urban construction, agricultural production, and industrial activities will generate a large amount of carbon emissions, while ecological land such as forests, grasslands, and wetlands will serve as carbon sinks.

[0003] In land use, scholars often use tools such as land use dynamics models, transfer matrices, and PLUS models to explore the spatiotemporal evolution characteristics of watershed land use, or combine InVEST models to analyze habitat quality changes. For example, studies on the Liuchong River Basin and the Pinglu Canal Economic Belt have revealed the interactive impact of the natural environment and human activities on land use. In the field of carbon source and sink research, research perspectives cover resource development, production technology innovation, and policy management. Some studies also take typical watersheds such as the Yellow River Basin and the Yangtze River Economic Belt as examples, using Markov models and carbon offset models to quantify the spatial differentiation of ecosystem carbon storage or carbon emissions. Carbon source and sink accounting methods are showing a diversified trend, ranging from macro-level carbon emission distribution mapping based on nighttime light data, energy consumption, and traffic flow, to micro-level measurement methods optimized for soil carbon pools, water conservancy projects, and forest management. Furthermore, models that fuse artificial intelligence and multi-source data have been applied to watershed carbon emission simulation and prediction.

[0004] Current research still has shortcomings and is insufficient to meet the needs of accurate carbon source and sink assessment and low-carbon planning at the watershed scale:

[0005] Most studies have separated land use change from carbon source and sink accounting, failing to establish a sufficient dynamic correlation mechanism between the two. This has led to biases in carbon emission assessment and prediction results, making it impossible to accurately reflect the actual impact of land use on the carbon cycle.

[0006] Existing research often focuses on single changes in traditional land types (such as arable land and forest land), neglecting the land type transformation patterns of the "production-living-ecology" three-dimensional space under the perspective of national land spatial planning. It also lacks a comprehensive analysis of resource elements (such as transportation networks and population distribution) and the carbon effect of land use, making it difficult to support the optimal allocation of national land space.

[0007] The accounting method is mainly based on a macro-level "top-down" model, relying on regional-scale statistical data (such as total energy consumption and total population). This easily overlooks the differences in carbon sources and sinks of micro-plots, leading to distorted carbon calculation results for some regions or land use types, which cannot meet the needs of refined management. Summary of the Invention

[0008] To address the aforementioned shortcomings in existing technologies, this invention provides a watershed land use carbon source and sink assessment method that solves the problems of insufficient coupling between land and carbon source and sink research, limited perspective of land use research, limited accuracy of carbon source and sink accounting, and lack of low-carbon orientation in future scenario setting in existing carbon source and sink assessment technologies.

[0009] To achieve the aforementioned objectives, the technical solution adopted by this invention is as follows: a method for assessing carbon sources and sinks in watershed land use, characterized by comprising the following steps:

[0010] S1. Construct a land use classification and grading system, dividing land use types into cultivated land, urban land, rural settlements, industrial and mining land, water areas, forest land, grassland and unused land;

[0011] S2, based on multi-source data, obtains carbon source and carbon sink driving data for various land use types. The driving data includes energy consumption data, population data, crop yield data, livestock and poultry breeding data, transportation data, and natural geographic data.

[0012] S3. Based on different land use types, corresponding carbon source and carbon sink accounting models are used to calculate carbon content: cultivated land uses the agricultural carbon source and carbon sink accounting model; urban land and rural settlements use the population respiratory carbon emission model; industrial and mining land uses the fossil energy carbon emission model; water areas use the shipping carbon emission and aquatic carbon sink model; forest land, grassland and unused land use the carbon coefficient method carbon sink model.

[0013] S4 simulates and predicts the carbon source and sink of watershed land use. It selects driving factors and sets four scenarios: natural development, farmland protection, economic development, and low-carbon orientation. Using the PLUS model, it inputs historical land use data and driving factor data, sets model parameters, and simulates the future land use of the watershed.

[0014] S5, combining the accounting methods of S4 and S3, outputs the watershed land use carbon source and sink assessment results, including spatial distribution maps, statistical tables, and scenario comparison analysis.

[0015] Furthermore, in the aforementioned watershed land use carbon source and sink assessment method, the driving factors mentioned in S4 include natural geographical factors, socio-economic factors, and accessibility factors; natural geographical factors include elevation, slope, and aspect; socio-economic factors include population density and GDP per capita; slope and aspect include the slope and aspect of highways, national highways, provincial highways, county highways, railways, water systems, and settlements; the natural development scenario is based on watershed land use changes from 2000 to 2022, using Markov chains to predict the demand for various land types in 2030; the farmland protection scenario merges stable farmland and high-quality farmland into restricted conversion zones, and modifies the Markov transition probability matrix; the economic development scenario modifies the Markov transition probability matrix to promote the expansion of urban land and industrial and mining land; the low-carbon orientation scenario sets nature reserves as restricted conversion zones, and modifies the Markov transition probability matrix to strengthen ecological protection and low-carbon development.

[0016] Furthermore, in the aforementioned watershed land use carbon source and sink assessment method, the agricultural carbon source and sink accounting model described in S3 includes an agricultural carbon source accounting formula and an agricultural carbon sink accounting formula; the agricultural carbon source accounting formula is: In the formula: Represents carbon sources in agricultural arable land; Represents the number of livestock and poultry breeds raised; The conversion coefficient representing the carbon source factor of livestock and poultry; The amount of carbon source factors used in agricultural production; The carbon source factor conversion coefficient represents agricultural production; the formula for agricultural carbon sink accounting is: In the formula: C represents carbon sequestration in agricultural arable land; Let i be the carbon uptake rate of the i-th crop; For the economic yield of crops; Di, The moisture content of the crop; This is the crop economic coefficient.

[0017] Furthermore, in the aforementioned watershed land use carbon source and sink assessment method, the carbon source accounting formula for urban land use and rural settlements described in S3 is as follows: ; Provide a source of carbon for breathing for urban or rural residents; This refers to the number of permanent residents in urban / rural areas. The carbon emission coefficient for human respiration is 79.0 kg. (person a) -1 .

[0018] Furthermore, in the aforementioned watershed land use carbon source and sink assessment method, the carbon source calculation formula for industrial and mining land mentioned in S3 is as follows: = ;in, Let i be the final consumption of the i-th type of energy. This is the standard coal conversion factor. For carbon emission coefficients, GDP a and GDP b These are the regional gross domestic product (GDP).

[0019] Furthermore, in the aforementioned watershed land use carbon source and sink assessment method, the water area carbon source accounting formula described in S3 is as follows: S represents shipping distance, and T represents freight volume. and These are the standard coal conversion factor and carbon emission factor for energy, respectively.

[0020] Furthermore, in the aforementioned watershed land use carbon source and sink assessment method, the carbon sink accounting formulas for forest land, grassland, and unused land mentioned in S3 are as follows: ; For land type area, This corresponds to the carbon sink coefficient.

[0021] The beneficial effects of this invention are as follows: By constructing a carbon source and carbon sink accounting model that directly corresponds to land use type, this invention dynamically links land use change with the carbon cycle process, overcoming the problem of separation between the two in traditional research, and significantly improving the accuracy and real-world reflective ability of carbon source and sink assessment.

[0022] The method incorporates the "three-life spaces" types, including urban and rural settlements, industrial and mining land, and water areas, and combines multi-source driven data (such as energy, population, and transportation) to systematically reveal the comprehensive impact of different land use patterns on carbon sources and sinks, providing a scientific basis for optimizing national land space.

[0023] By adopting a classification and grading accounting model and combining plot-scale driving data, accurate carbon measurement from macro to micro levels can be achieved, effectively avoiding assessment bias caused by relying on regional statistical averages and meeting the needs of refined management and policy formulation. Detailed Implementation

[0024] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0025] This embodiment takes the Huai River Basin as the research object. Based on land use data, socio-economic data, and physical geographic data from 2000 to 2022, carbon source and sink assessment and prediction are carried out according to the method described in this invention. The specific steps are as follows:

[0026] I. Data Preparation and Preprocessing

[0027] Land use data: Five periods of land use data were obtained for the Huai River Basin in 2000, 2010, 2015, 2018 and 2022. A land use classification and grading system was constructed, which is divided into eight categories: cultivated land, urban land, rural settlements, industrial and mining land, water area, forest land, grassland and unused land. The accuracy of the data was verified by field sampling, and the accuracy rate reached more than 95%.

[0028] Collect multi-source driving data, specifically including:

[0029] Energy consumption data: covering the final consumption of seven types of energy: raw coal, coke, crude oil, diesel, fuel oil, heat, and electricity;

[0030] Population data: Obtain the number of urban / rural permanent residents from 2000 to 2022;

[0031] Crop yield data: including the yields of major crops such as vegetables, rice, tobacco, fruits, rapeseed, wheat, corn, potatoes, and beans;

[0032] Livestock and poultry farming data: Obtain the annual inventory and slaughter volume of livestock, cattle, sheep, poultry and other farmed species;

[0033] Traffic data: Obtain inland waterway mileage and freight volume in the Huai River Basin, as well as spatial distribution data of expressways, national highways, provincial highways, county roads, and railways;

[0034] Natural geographic data: Extract watershed elevation, slope, aspect data, as well as spatial location information of water systems and settlements;

[0035] Socioeconomic data: including population density, per capita GDP, and regional gross domestic product (GDPa, GDPb) of each district and county within the basin from 2000 to 2022.

[0036] All spatial data (land use data, natural geographic data, and transportation data) were uniformly converted to Albers equal-area conic projection, with the coordinate system adopted as CGCS2000 and the raster resolution uniformly set to 30m×30m to ensure spatial consistency of the data.

[0037] Socioeconomic data such as population density and GDP per capita are normalized to eliminate the influence of dimensions; energy consumption data and crop yield data are converted to standard units such as "t" and "kg"; slope data are graded into levels of 0-2°, 2-6°, 6-15°, 15-25°, and >25° for subsequent extraction of high-quality arable land in arable land protection scenarios.

[0038] II. Carbon Source and Sink Accounting

[0039] Based on the accounting models corresponding to different land use types, the carbon source and carbon sink of various land uses in the Huai River Basin in 2022 were calculated respectively:

[0040] The formula for calculating agricultural carbon sources is: In the formula: Represents carbon sources in agricultural arable land; Represents the number of livestock and poultry breeds raised; The conversion coefficient representing the carbon source factor of livestock and poultry; The amount of carbon source factors used in agricultural production; The carbon source factor conversion coefficient representing agricultural production.

[0041] Livestock and poultry farming carbon sources: Given that in 2022, the Huai River Basin had 8.5 million pigs, 1.2 million cattle, 6.8 million sheep, and 320 million poultry, and considering the livestock and poultry carbon source factor conversion coefficient (pig 3.01 × 10⁻⁶),... 2 kg ind -1 a -1 2.92 × 10 3 kg ind -1 a -1 1.28 × 10 sheep 1 kg ind -1 a -1 Poultry 2.37×10 2 kg ind -1 a -1 The total carbon source for livestock and poultry farming was calculated to be 850 × 10⁻⁶. 4 ×301+120×10 4 ×2920+680×10 4 ×12.8+3.2×10 8 ×237=8.92×10 7 t.

[0042] Carbon source for agricultural production: 8.2 × 10⁻⁶ acres of cultivated land in the Huai River Basin in 2022. 6 hm 2 Of which, paddy fields cover an area of ​​3.5 × 10 6 hm 2 Fertilizer usage: 1.2 × 10 7 t, Total power of agricultural machinery 3.8×10 7kW. Combined with the agricultural production carbon source factors in Table 1 (fertilizer CO2 emissions 8.57 × 10⁻⁶), 2 kg t -1 CO2 emissions from crop cultivation are 1.64 × 10 kg. hm -2 Agricultural machinery CO2 emissions: 0.180 kg kW -1 Irrigation CO2 emissions: 2.66 × 10 2 kg hm -2 CH4 emissions from paddy fields: 1.56 × 10⁻⁶ 2 kg hm -2 The total carbon source for agricultural production was calculated to be 1.2 × 10⁻⁶. 7 ×857+8.2×10 6 ×16.4+3.8×10 7 ×0.18+8.2×10 6 ×266+3.5×10 6 ×156=1.56×10 7 t.

[0043] Total carbon source of arable land: The carbon source from livestock and poultry farming and the carbon source from agricultural production are added together, resulting in a total carbon source of arable land in the Huai River Basin of 8.92 × 10⁻⁶ in 2022. 7 +1.56×10 7 =1.048×10 8 t.

[0044] The formula for agricultural carbon sequestration is: In the formula: C represents carbon sequestration in agricultural arable land; Let i be the carbon uptake rate of the i-th crop; For the economic yield of crops; Di, The moisture content of the crop; This is the crop economic coefficient.

[0045] Major crop yields in the Huai River Basin in 2022: Wheat 1.2 × 10⁻⁶ 7 t, corn 9×10 6 t, rice 8×10 6 t, vegetables 5×10 6Based on the crop parameters in Table 2 (wheat carbon absorption rate 0.490, moisture content 0.120, economic coefficient 0.400; maize carbon absorption rate 0.470, moisture content 0.130, economic coefficient 0.400; rice carbon absorption rate 0.420, moisture content 0.120, economic coefficient 0.450; vegetable carbon absorption rate 0.450, moisture content 0.900, economic coefficient 0.600), calculate the carbon sink of each crop:

[0046] Wheat carbon sequestration: (0.490×1.2×10 7 ×(1-0.120)) / 0.400=1.2936×10 7 t;

[0047] Corn carbon sequestration: (0.470×9×10 6 ×(1-0.130)) / 0.400=8.97225×10 6 t;

[0048] Rice carbon sequestration: (0.420×8×10 6 ×(1-0.120)) / 0.450=6.5856×10 6 t;

[0049] Vegetable carbon sequestration: (0.450×5×10 6 ×(1-0.900)) / 0.600=3.75×10 5 t;

[0050] Total carbon sequestration of arable land is the sum of carbon sequestration from all types of crops, i.e., 1.2936 × 10⁻⁶. 7 +8.97225×10 6 +6.5856×10 6 +3.75×10 5 =2.886885×10 7 t.

[0051] The formula for carbon source accounting for urban land use and rural settlements is as follows: ; Provide a source of carbon for breathing for urban or rural residents; This refers to the number of permanent residents in urban and rural areas. The carbon emission coefficient for human respiration is 79.0 kg. (person a) -1 .

[0052] The urban resident population in the Huai River Basin in 2022 was 1.2 × 10⁻⁶. 7 The rural resident population is 2.1 × 10⁻⁶. 7 The carbon emission coefficient of human respiration is 79.0 kg. (person a) -1 .

[0053] Carbon source from urban land use: 1.2 × 10⁻⁶ 7 ×79.0×10 -3 =9.48×10 5 t;

[0054] Carbon source in rural settlements: 2.1 × 10 7 ×79.0×10 -3 =1.659×10 6 t;

[0055] The total carbon source for the two land use categories is 9.48 × 10⁻⁶. 5 +1.659×10 6 =2.607×10 6 t.

[0056] The formula for calculating carbon sources in industrial and mining land is: = ;in, Let i be the final consumption of the i-th type of energy. This is the standard coal conversion factor. denoted as the carbon emission coefficient, and GDPa and GDPb are the regional gross domestic product, respectively.

[0057] Table 1 shows the main energy consumption and parameters of industrial and mining land in the Huai River Basin in 2022. A typical county within the basin (GDPa = 80 × 10) was selected. 8 (yuan) and the entire basin (GDPb=1.2×10) 4 ×10 8 The amount is calculated in yuan.

[0058] Table 1:

[0059]

[0060] Calculate the carbon source of industrial and mining land in this district / county:

[0061] Carbon emissions from raw coal: 5 × 10 6 ×0.7143×0.7559×(80 / 12000)=1.79×10 4 t;

[0062] Coke carbon emissions: 8×10 5 ×0.9714×0.8550×(80 / 12000)=4.37×10 3 t;

[0063] Crude oil carbon emissions: 1.2 × 10 6×1.4286×0.5857×(80 / 12000)=6.69×10 3 t;

[0064] Diesel carbon emissions: 6×10 5 ×1.4571×0.5921×(80 / 12000)=3.41×10 3 t;

[0065] Carbon emissions from fuel oil: 3 × 10 5 ×1.4286×0.6185×(80 / 12000)=1.75×10 3 t;

[0066] Thermal carbon emissions: 2×10 9 ×0.0341×0.7730×(80 / 12000)×10 -3 =3.51×10 3 t;

[0067] Carbon emissions from electricity: 5 × 10 9 ×0.1229×0.2132×(80 / 12000)×10 -3 =9.02×10 3 t;

[0068] The total carbon source of industrial and mining land in this district is 1.79 × 10⁻⁶. 4 +4.37×10 3 +6.69×10 3 +3.41×10 3 +1.75×10 3 +3.51×10 3 +9.02×10 3 =4.665×10 4 t.

[0069] Based on this method, the total carbon source of industrial and mining land in the Huai River Basin in 2022 is estimated to be 7.2 × 10⁻⁶. 6 t.

[0070] The formula for calculating carbon sources in water bodies is: S represents shipping distance, and T represents freight volume. and These are the standard coal conversion factor and carbon emission factor for energy, respectively.

[0071] In 2022, the inland waterway navigable mileage in the Huai River Basin was S = 5 × 10 3 kilometers, freight volume T=8×10 7 The shipping volume is 1.4571 kgce / kg, and the carbon emission coefficient is 0.5921. The shipping mainly uses diesel as an energy source.

[0072] Carbon source in water: 5×10 3 ×8×10 7 ×1.4571×0.5921×10 -9 =3.48×10 5 t (Note: During the unit conversion process, kilometers and tons are converted to a unified dimension, and the final result is in "t").

[0073] The formulas for carbon sequestration accounting for forest land, grassland, and unused land are as follows: ; For land type area, This corresponds to the carbon sink coefficient.

[0074] The forest area in the Huai River Basin in 2022 was 3.5 × 10⁻⁶. 6 hm 2 Grassland area 1.2×10 6 hm 2 Unused land area 8×10 5 hm 2 The corresponding carbon sink coefficients are 5.2 t / (hm). 2 a) 2.1t / (hm 2 a) 0.3t / (hm 2 a).

[0075] Forest carbon sequestration: 3.5 × 10 6 ×5.2=1.82×10 7 t;

[0076] Grassland carbon sequestration: 1.2 × 10⁻⁶ 6 ×2.1=2.52×10 6 t;

[0077] Unused land carbon sequestration: 8×10 5 ×0.3=2.4×10 5 t;

[0078] The total carbon sequestration of the three land use categories is 1.82 × 10⁻⁶. 7 +2.52×10 6 +2.4×10 5 =2.096×10 7 t.

[0079] Table 2 shows the total carbon sources and sinks in the Huai River Basin in 2022.

[0080] Table 2:

[0081]

[0082] III. Future Land Use and Carbon Source / Sink Simulation and Prediction

[0083] Natural geographical factors (elevation, slope, aspect), socio-economic factors (population density, GDP per capita), and accessibility factors (distance to highways, national roads, provincial roads, county roads, railways, water systems, and settlements) were selected as driving factors, and a 30m×30m raster dataset of driving factors was constructed based on a GIS platform.

[0084] Using the PLUS model, land use data from 2000 to 2022 was used as a basis to simulate the land use situation in 2022. The simulation was then compared with actual data for verification. The fitting result showed a kappa of 0.96 and an overall simulation accuracy of 0.979, meeting the prediction requirements. The model parameters were set as follows: new patch attenuation threshold 0.5, patch expansion coefficient 0.3, and seed percentage 0.02. The neighborhood weights for each land use type were set as follows: cultivated land 0.176, forest land 0.062, grassland 0.020, water area 0.238, urban land 0.381, rural settlements 0.030, industrial and mining land 0.080, and unused land 0.012.

[0085] Natural Development Scenario: Based on the land use change trend in the Huai River Basin from 2000 to 2022, without additional policy intervention, the demand for various land types in 2030 is predicted using Markov Chain, and the land transfer rules allow various types of land to be freely converted under natural driving forces.

[0086] Farmland protection scenario: Areas that were all farmland in the five periods from 2000 to 2022 are designated as stable farmland. Farmland with a slope of <6° is extracted as high-quality farmland and merged into restricted conversion zones. The Markov transition probability matrix is ​​modified to reduce the probability of farmland transitioning to urban land, rural settlements, and industrial and mining land by 70%, reduce the probability of transitioning to grassland and water areas by 40%, and increase the probability of unused land transitioning to farmland by 50%.

[0087] Economic development scenario: Modify the Markov transition probability matrix to reduce the probability of urban land and industrial / mining land transferring to other land uses by 40%, the probability of rural settlements transferring to other land uses by 30%, and increase the probability of forest land, grassland, water area, and unused land transferring to urban land and industrial / mining land (40%, 10%, 20%, and 50%, respectively).

[0088] Low-carbon oriented scenario: Nature reserves are designated as restricted conversion zones, prohibiting the conversion of forest land and grassland into urban and rural areas and industrial and mining land. The Markov transition probability matrix is ​​revised to reduce the probability of water bodies transitioning to urban land, rural settlements, and industrial and mining land by 40%, and increase the probability of water bodies transitioning to arable land by 30%; the probability of arable land transitioning to urban land, rural settlements, and industrial and mining land is reduced by 30%, the probability of unused land transitioning to urban land is increased by 40%, and the probability of urban land and rural settlements transitioning to forest land and grassland is increased by 10%.

[0089] The land use area (unit: hm²) in the Huai River Basin under four scenarios was obtained through PLUS model simulation. 2 (See Table 3).

[0090] Table 3:

[0091]

[0092] Based on the accounting model and the predicted land use area in 2030, the carbon source and sink (unit: t) under four scenarios are calculated, as shown in Table 4.

[0093] Table 4:

[0094]

[0095] IV. Output of Evaluation Results

[0096] Based on a GIS platform, spatial distribution maps of carbon sources and sinks in the Huai River Basin under four scenarios in 2022 and 2030 were drawn, clearly showing the differences in carbon source and sink intensity in different regions: high carbon source areas are mainly concentrated in urban built-up areas and industrial corridors with dense industrial and mining land, while high carbon sink areas are mainly distributed in mountainous areas covered by forests and wetland protection areas; under the low-carbon orientation scenario, the area of ​​high carbon sink areas expands significantly, and the expansion of high carbon source areas is effectively suppressed.

[0097] We compiled and output the 2022 carbon source and sink accounting statistics table for the Huaihe River Basin, the land use area statistics table under four scenarios in 2030, and the carbon source and sink prediction statistics table, which include key indicators such as the amount of carbon source, carbon sink, net carbon source and sink and their proportion for various types of land use.

[0098] Comparison of total carbon sources: Net carbon source in a low-carbon oriented scenario (5.22 × 10⁻⁶) 7 t) is the lowest, compared to the economic development scenario (8.52 × 10) 7 t) decreased by 38.7%, compared to the natural development scenario (6.75 × 10⁻⁶). 7 The t) decreased by 22.7%, indicating that low-carbon-oriented policies can effectively reduce watershed carbon emissions; the farmland protection scenario stabilizes farmland carbon sinks, resulting in a net carbon source of 6.18 × 10 7 The t) is lower than the natural development scenario, reflecting the positive role of farmland protection in carbon balance.

[0099] Land Use Type Impact Analysis: Under the economic development scenario, the area of ​​urban land and industrial and mining land expands significantly, leading to a significant increase in carbon sources from energy consumption and respiratory emissions, with a total carbon source of 1.35 × 10⁻⁶. 8 t is the highest among the four scenarios; under the low-carbon-oriented scenario, the area of ​​forest and grassland increases, and the carbon sink increases to 5.98 × 10⁻⁶. 7 At the same time, the total amount of carbon sources is reduced by restricting the expansion of land use for high-carbon sources.

[0100] Based on the scenario comparison results, the Huai River Basin should prioritize the implementation of low-carbon oriented land use planning, strengthen ecological protection in nature reserves, and expand the area of ​​forest and grassland. At the same time, it should also take into account the protection of arable land, strictly control the conversion of arable land to high-carbon source land, promote energy-saving transformation of industrial and mining land, and reduce carbon emissions from energy consumption.

Claims

1. A method for assessing carbon sources and sinks in watershed land use, characterized in that, Includes the following steps: S1. Construct a land use classification and grading system, dividing land use types into cultivated land, urban land, rural settlements, industrial and mining land, water areas, forest land, grassland and unused land; S2, based on multi-source data, obtains carbon source and carbon sink driving data for various land use types. The driving data includes energy consumption data, population data, crop yield data, livestock and poultry breeding data, transportation data, and natural geographic data. S3. Based on different land use types, corresponding carbon source and carbon sink accounting models are used to calculate carbon content: cultivated land uses the agricultural carbon source and carbon sink accounting model; urban land and rural settlements use the population respiratory carbon emission model; industrial and mining land uses the fossil energy carbon emission model; water areas use the shipping carbon emission and aquatic carbon sink model; forest land, grassland and unused land use the carbon coefficient method carbon sink model. S4 simulates and predicts the carbon source and sink of watershed land use. It selects driving factors and sets four scenarios: natural development, farmland protection, economic development, and low-carbon orientation. Using the PLUS model, it inputs historical land use data and driving factor data, sets model parameters, and simulates the future land use of the watershed. S5, combining the accounting methods of S4 and S3, outputs the watershed land use carbon source and sink assessment results, including spatial distribution maps, statistical tables, and scenario comparison analysis.

2. The watershed land use carbon source and sink assessment method according to claim 1, characterized in that, The driving factors described in S4 include natural geographical factors, socio-economic factors, and accessibility factors. Natural geographical factors include elevation, slope, and aspect; socio-economic factors include population density and GDP per capita; slope and aspect include the slope and aspect of highways, national highways, provincial highways, county highways, railways, water systems, and settlements; the natural development scenario is based on watershed land use change from 2000 to 2022, using Markov chains to predict the demand for various land types in 2030; the farmland protection scenario merges stable farmland and high-quality farmland into restricted conversion zones, and modifies the Markov transition probability matrix; the economic development scenario modifies the Markov transition probability matrix to promote the expansion of urban and industrial land use; the low-carbon orientation scenario sets nature reserves as restricted conversion zones, and modifies the Markov transition probability matrix to strengthen ecological protection and low-carbon development.

3. The watershed land use carbon source and sink assessment method according to claim 1, characterized in that, The agricultural carbon source and carbon sink accounting model described in S3 includes an agricultural carbon source accounting formula and an agricultural carbon sink accounting formula; the agricultural carbon source accounting formula is as follows: In the formula: Represents carbon sources in agricultural arable land; Represents the number of livestock and poultry breeds raised; The conversion coefficient representing the carbon source factor of livestock and poultry; The amount of carbon source factors used in agricultural production; The carbon source factor conversion coefficient represents agricultural production; the formula for agricultural carbon sink accounting is: In the formula: C represents carbon sequestration in agricultural arable land; Let i be the carbon uptake rate of the i-th crop; For the economic yield of crops; Di, The moisture content of the crop; This is the crop economic coefficient.

4. The watershed land use carbon source and sink assessment method according to claim 3, characterized in that, The carbon source accounting formula for urban land use and rural settlements described in S3 is as follows: ; Provide a source of carbon for breathing for urban or rural residents; This refers to the number of permanent residents in urban / rural areas. The carbon emission coefficient for human respiration is 79.0 kg / (person·a). -1 .

5. The watershed land use carbon source and sink assessment method according to claim 4, characterized in that, The formula for calculating carbon sources in industrial and mining land as described in S3 is: = ;in, Let i be the final consumption of the i-th type of energy. This is the standard coal conversion factor. For carbon emission coefficients, GDP a and GDP b These are the regional gross domestic product (GDP).

6. The watershed land use carbon source and sink assessment method according to claim 5, characterized in that, The formula for calculating carbon sources in water bodies as described in S3 is: ; S represents shipping distance, and T represents cargo volume. and These are the standard coal conversion factor and carbon emission factor for energy, respectively.

7. The watershed land use carbon source and sink assessment method according to claim 6, characterized in that, The carbon sequestration accounting formulas for forest land, grassland, and unused land mentioned in S3 are as follows: ; For land type area, This corresponds to the carbon sink coefficient.

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