Carbon emission accounting method and application thereof in illicium verum forest management and transformation process

Abstract

The invention belongs to the field of forestry, and discloses an octagonal forest management and low-change process carbon emission accounting method, and the method comprises the following steps for different forest stand octagonal forest management modes: determining a target range, a function unit and a system boundary; collecting a data list; constructing a life cycle list; modeling a carbon footprint; sensitivity analysis and data quality analysis; the method aims at providing an accurate and efficient carbon emission accounting system so as to promote sustainable operation of the illicium verum forest and protection of the ecological environment.

Description

[0001] This application is a divisional application. The original application was entitled "A Method for Calculating Carbon Emissions in the Management and Low-Carbon Improvement Process of Star Anise Forests," with application number 202411548480.5 and application date of November 1, 2024. Technical Field

[0002] This invention belongs to the field of forestry, specifically relating to a carbon emission accounting method for the management and low-carbon transformation of star anise forests. Background Technology

[0003] Star anise (Illicium verum Hook.f.) is an evergreen broad-leaved tree belonging to the genus Illicium in the family Illicaceae. Star anise is a highly valued aromatic medicinal plant; its fruit is not only an important traditional Chinese medicine but also a commonly used spice in cooking.

[0004] Star anise forest is a general term for both planted star anise forests and public welfare star anise forests. Non-inefficient, low-yield star anise forests refer to those that, through reasonable tending and fertilization management, achieve a yield of 750 kg / hm². 2 The canopy density was between 0.5 and 0.8, and there were no obvious pests or diseases. However, due to the low price of star anise in the early stages, farmers neglected management, resulting in excessive planting density, overgrown weeds, and a lack of investigation and testing of star anise pests and diseases. This led to a decrease in star anise yield from about 10 kg / tree to 2-3 kg / tree. Therefore, low-yield restoration of star anise forests is particularly important. Low-yield restoration refers to supplementary forestry management activities implemented through forestry engineering measures such as variety improvement, forest stand transformation, and soil improvement to restore low-quality and low-efficiency forest stands to normal levels, increase production and income, and improve quality and efficiency.

[0005] Carbon emissions, namely greenhouse gases released into the atmosphere during human activities, are one of the key drivers of global climate change.

[0006] Controlling carbon emissions from economic forests is crucial for achieving sustainable economic and environmental development. For example, CN116349539 A discloses a management method for promoting flowering, fruiting, and increasing yield in mature star anise forests. This method involves managing mature star anise forests and selecting different measures based on the different characteristics of star anise at different growth stages to improve the yield and quality of the forests. Lu Daodiao et al. proposed star anise forest resources and management strategies in the Liuwan Mountain area of ​​Guangxi, classifying star anise forests for management and implementing related practical measures to improve the overall benefits of forestry and promote sustainable economic development. Numerous studies on carbon emissions have emerged. For instance, CN116303799A discloses a blockchain-based forestry carbon emission accounting method, which first determines the life cycle of trees and then divides the production process into several carbon emission unit modules. Ding Sheng et al. divided the forestry industry in Jiangsu Province into 18 subcategories and studied the carbon emissions of the Jiangsu forestry industry based on a Logistic model. However, current accounting for forest carbon emissions mainly focuses on the macro level of the entire forestry industry, often neglecting detailed considerations for specific forest species. While this general accounting method provides a rough overview of carbon emissions, it fails to accurately reflect the specific roles and contributions of different forest types in the carbon cycle. Taking star anise forests as an example, a detailed classification based on factors such as site conditions and yield levels is necessary. Based on this, customized management strategies can be proposed for each type of star anise forest, and production efficiency can be improved through improvement measures. Simultaneously, detailed carbon emission calculations should be conducted for each management model to ensure that management measures both promote the sustainable development of star anise forests and maximize their function as carbon sinks.

[0007] Carbon emissions accounting is a key step in assessing and monitoring carbon emissions. It not only helps to understand the current carbon emissions situation, but also helps to predict future carbon emissions trends.

[0008] The boundary of a carbon emissions accounting system refers to the process of quantifying the greenhouse gases directly and indirectly generated by activities throughout the life cycle of forest products, from seed to product. This invention develops detailed carbon emissions accounting procedures for different forest stand types, management processes, and technologies to ensure accurate assessment of carbon emissions from star anise forest management and low-carbon transformation models.

[0009] The carbon footprint of forest products encompasses the entire lifecycle of carbon emissions, from seed development to product use and disposal. This invention focuses on the lifecycle emissions process from seedling to product harvest, including the stand management and production, product processing, and use stages. The stand management and production stage is particularly critical, requiring comprehensive consideration of factors such as tree species selection, management methods, natural factors, and management practices.

[0010] In summary, this invention aims to quantitatively assess carbon emissions from different management processes, forest land types, and yield and quality grading of star anise forests, in order to provide a scientific basis for the sustainable development of star anise forests. Summary of the Invention

[0011] To address the aforementioned technical problems, this invention provides a method for carbon emission accounting in the management and low-carbon transformation process of star anise forests, thereby resolving the issues in the prior art. The technical solution adopted by this invention is as follows:

[0012] The carbon emission accounting method for the management and low-carbon transformation of star anise forests includes the following steps:

[0013] S1. Classify star anise forests according to soil fertility level, yield, etc.; determine the target range, functional units, and system boundaries;

[0014] S2. Data list collection;

[0015] S3. Lifecycle inventory construction;

[0016] S4. Carbon footprint modeling;

[0017] S5. Carbon emission calculation;

[0018] S6. Sensitivity Analysis;

[0019] S7. Data quality analysis.

[0020] Furthermore, star anise forests include: non-inefficient and low-yield star anise forests, inefficient star anise forests, mildly low-yield star anise forests, moderately low-yield star anise forests, and severely low-yield star anise forests.

[0021] Furthermore, the management and improvement process of star anise forests includes: land preparation, afforestation, tending, and harvesting.

[0022] Furthermore, in the process of defining the technical framework:

[0023] The objectives include reducing carbon emissions from star anise forests, increasing carbon sequestration capacity, or assessing the carbon benefits of low-carbon transformation measures.

[0024] The functional unit includes carbon emissions per hectare of forest land;

[0025] The system boundary includes all inputs and outputs in the accounting process.

[0026] Furthermore, the data list collection includes:

[0027] The raw data includes the input and consumption of production materials, energy consumption, nursery time, and material transportation.

[0028] Furthermore, the lifecycle manifest construction includes:

[0029] On-site energy use;

[0030] The substrate consists of red clay and coconut fiber;

[0031] Synthetic fertilizers and chemicals used in production;

[0032] Consumables used in production;

[0033] Soil discharge.

[0034] Furthermore, carbon footprint modeling includes:

[0035] General formula for calculating the carbon footprint of star anise forest: CF ST =CF seeding +CF tree ,

[0036] Among them CF ST Carbon footprint of star anise forest; CF seedlin g represents the carbon footprint of star anise seedling production; CF tree Carbon footprint of Octagon during its operation;

[0037] Carbon footprint calculation formula: E=∑i(G i ×EF ij )

[0038] Where E represents the greenhouse gas emissions of functional unit j; G i Data on the activity usage of input i; EF ij The emission factor of input i for greenhouse gas j;

[0039] CF=∑i(E i ×GWP j )

[0040] Where CF represents the carbon footprint of the functional unit; E j denoted as functional unit j, where GWPj is the global warming potential value, which is the coefficient that correlates the effect of a unit mass of greenhouse gas j at a given internal radiation intensity with the effect of an equal amount of CO2 radiation.

[0041] Carbon footprint of electricity: E Elec =G Elec ×EF Elec

[0042] Among them, E Elec CO2 emissions corresponding to the use of electricity by the functional unit; G Elec Power consumption for functional units; EF Elec Emissions factors in electricity production;

[0043] Carbon footprint of fuel:

[0044] Among them, E Fuel CO2 emissions corresponding to the use of fuel by the functional unit; U ma Let f be the fuel consumption of machine m per unit time; ef mT represents the energy conversion efficiency of machine m. m EF is the working time of machine m. a The emission factor of greenhouse gases produced by fuel a; Q a The average lower heating value of fuel a;

[0045] Fertilizer carbon footprint: E fer =∑i(A x ×M x ×f x ×EF x )

[0046] Among them, E fer CO2 emissions corresponding to the use of fertilizer by the functional unit; A x M represents the area where fertilizer x is applied; x f represents the amount of fertilizer applied per unit area in a single application; x EF represents the average number of fertilizations for fertilizer x; x Fertilizer x is a greenhouse gas emission factor;

[0047] Chemical carbon footprint: E cha =∑i(AD) x ×EF x )

[0048] Among them, E cha CO2 emissions corresponding to the use of chemicals in the functional unit; AD x Data on the use of chemical x; EF x The greenhouse gas emission factor for chemical x;

[0049] Material carbon footprint: E mat =∑i(AD) x ×EF x )

[0050] Among them, E mat The CO2 emissions corresponding to the materials used in the functional unit; AD x For the usage activity data of material x; EF x The emission factor of greenhouse gases generated by material x;

[0051] Transportation carbon footprint: E T =M T ×G T ×EF T

[0052] Among them, E T CO2 emissions corresponding to the electricity used by the functional unit; G T For the weight of the transport functional unit; G T For the transport distance of the functional unit; EFT Emissions from transportation production.

[0053] Furthermore, the following formula is used for sensitivity analysis:

[0054] E i =△A i / △F i

[0055] E i This represents the input variable F. i Sensitivity coefficient;

[0056] ΔF i This represents the rate of change of the input variable F;

[0057] ΔA i This indicates that when the input variable F i The rate of change is ΔF i At that time, the rate of change of evaluation result A;

[0058] |E| represents the evaluation result A. i For input variable F i Sensitivity to change;

[0059] It includes the following steps: listing the variables required for the calculation, calculating the sensitivity coefficient, and comparing the results.

[0060] Furthermore, data quality analysis includes the following steps:

[0061] Identify and determine key data; perform data feature analysis and matrix construction; calculate data quality scores; interpret and apply data quality analysis results; provide specific examples of data quality analysis.

[0062] The present invention has the following beneficial effects:

[0063] (1) Camellia oil plays an important role in the economic forest industry and provides solid support for the development of national strategies. Carbon footprint accounting of the camellia oil production process helps to improve the sustainability, resource utilization efficiency and industrial competitiveness of the economic forest industry.

[0064] (2) Balance detailed operation and improve accuracy: The two subsystems of afforestation and primary oil extraction are identified. The operation process in forest management and product production is considered. The process division is detailed and close to actual operation, which balances the accuracy and operability of carbon footprint accounting.

[0065] (3) Reflecting the key aspects of the system and improving application value: It effectively reflects the key aspects of tea oil production and comprehensively considers social and environmental impacts, including social transportation costs and ecosystem services. The comprehensive analysis is thorough and close to reality, making carbon footprint accounting more specific and operable, providing practical application value, and providing precise guidance and decision support for the sustainable development of economic forestry industry.

[0066] (4) Multiple emission data to improve the reliability of calculation: Based on actual data, the emission differences of different inputs in tea oil production and transportation are fully considered. By accurately capturing the impact of tea oil production on carbon footprint, the reliability of carbon footprint accounting is significantly improved.

[0067] (5) Differentiated calculation, significantly improving accuracy: Considering the differences in the production process caused by different production management methods, the production methods are calculated separately, which significantly improves the accuracy of carbon footprint accounting compared to simplifying it into a general category of patents;

[0068] (6) Analyze emission proportions and accurately quantify sources: By conducting detailed analysis of emission proportions, the emission sources of different activities or processes can be accurately classified and quantified, providing more insightful and practical carbon footprint information for industrial decision-making and promoting the sustainable development of forestry production in my country.

[0069] (7) Reduce error rate and improve data quality: Reduce potential errors and effectively reduce the error rate in low carbon footprint accounting, thereby improving the overall data quality, providing a more reliable and accurate data foundation for carbon footprint accounting of tea oil production, and providing more trustworthy support for industry decision-making;

[0070] (8) Clearly present key information and make it easy to understand and use: comprehensively display key factors, including uncertainty, sensitivity and emission ratio, so that practitioners can more easily understand and make full use of carbon footprint data, which promotes the practical application of carbon footprint accounting and the accuracy of industry decision-making.

[0071] (9) Considering emissions from different work methods, providing targeted recommendations for reducing carbon footprint: Considering the specific carbon emission factors of different work methods, more accurate calculation results are provided. By deeply analyzing the different environmental adaptability of manual and mechanical operations, the carbon emission sources in the production process are comprehensively captured, providing more operational and targeted recommendations for reducing carbon footprint.

[0072] (10) Focus on the introduction of new technologies and innovatively solve carbon footprint challenges: It not only considers the improvement of existing technologies, but also focuses on the introduction of new technologies to address the challenges of carbon footprint in tea oil production, such as advanced pesticide spraying technology (drone spraying) and optimized traffic processes (emission factor differences in different road transport processes), making the overall accounting system more comprehensive; the comprehensive application of these new technologies is more innovative in the carbon footprint accounting of tea oil production.

[0073] (11) Combining energy system transformation to provide comprehensive carbon footprint information for the entire industrial chain: It brings significant advantages based on existing databases and algorithms; By combining energy system transformation, it not only focuses on carbon emissions in the production process, but also deeply considers the environmental protection of the energy system, providing more comprehensive carbon footprint information for the entire industrial chain; The comprehensive consideration of transportation, energy, supply chain, industrial chain, etc., captures the sources of carbon emissions more comprehensively based on existing databases, providing decision-makers with more detailed and practical data; This systematic optimization not only provides a more accurate data foundation for carbon footprint accounting, but also provides strong support for the development of green economy and the construction of sustainable industrial chains;

[0074] (12) Compared with existing patents on life cycle assessment of agricultural and forestry products, the life cycle emission inventory construction method of this invention adopts a more detailed and comprehensive approach; carbon emissions of each production link are finely divided, making the emission inventory more detailed and practical, providing decision-makers with more comprehensive carbon footprint data; for environmental cost calculation, by deeply analyzing the environmental costs of each stage, including emissions of energy, materials and production processes, the calculation of environmental costs is more accurate and comprehensive, which provides decision-makers with more detailed economic and environmental information, contributing to more sustainable economic development; supply chain carbon emission accounting, by deeply understanding the carbon emission situation of each link in the supply chain, from raw material procurement to final product delivery, the emission data is more accurate, which helps to comprehensively optimize the supply chain and reduce the overall carbon footprint. Attached Figure Description

[0075] Figure 1 This is a flowchart of the carbon footprint accounting method for camellia oil production, from afforestation to virgin oil extraction;

[0076] Figure 2 This is a flowchart of the afforestation subsystem;

[0077] Figure 3 This is a flowchart of the virgin oil subsystem;

[0078] Figure 4 This is a boundary map of the carbon footprint accounting system for camellia oil production from afforestation to primary oil extraction;

[0079] Figure 5 It is a boundary map of the afforestation subsystem;

[0080] Figure 6 This is the boundary diagram of the virgin oil subsystem. Detailed Implementation

[0081] The following will be based on embodiments of the present invention. Figures 1-6 The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0082] like Figure 1 This invention relates to a carbon emission accounting method for the management and low-yield improvement process of star anise forests. The method involves the following steps for different management models of star anise forest stands: S1: Classifying star anise forests according to soil fertility levels, yields, etc.; determining the target scope, functional units, and system boundaries; S2: Data inventory collection; S3: Life cycle inventory construction; S4: Carbon footprint modeling; S5: Sensitivity analysis; and S6: Data quality analysis. This method aims to provide an accurate and efficient carbon emission accounting system to promote the sustainable management of star anise forests and the protection of the ecological environment.

[0083] Star anise forest management and low-yield improvement refers to a collection of human forestry activities that differentiate star anise forests based on soil fertility and yield performance, including non-low-yield, low-yield, slightly low-yield, moderately low-yield, and severely low-yield star anise forests.

[0084] Non-inefficient and low-yield star anise forests refer to planted or public welfare forests of star anise without any human or natural factors causing low soil fertility, and whose forest product yield is ≥750 kg / hm². 2 The canopy closure is between 0.5 and 0.8, and there are no obvious pests or diseases. The tree species selection and management methods are reasonable and the trees are suitable for the site.

[0085] Low-efficiency star anise forests refer to star anise forests that, due to human or natural factors, show significant decline in tree growth and low functional benefits compared to star anise forests under the same environmental conditions. In planted forests, the species are poor and the trees are aging, resulting in a significant decrease in forest product yield. In public welfare forests, the climate is highly vulnerable, the ability to resist natural disasters such as fires, windstorms, and floods is poor, the forest appearance is dilapidated, the canopy density is <0.5, and the ecological function is low.

[0086] Mildly low-yielding star anise forests refer to forests with a canopy closure greater than 0.8, a fruit set rate greater than 70%, and a forest product yield less than 750 kg / hm², caused by factors such as unreasonable management methods, poor afforestation quality, poor site conditions, extensive management, and improper tending. 2 The star anise forest;

[0087] Moderately low-yielding star anise forests refer to forests with a canopy density of 0.6 ≤ canopy closure < 0.8, a fruit set rate of 40 ≤ fruit set < 70%, and a forest product yield of < 750 kg / hm², caused by factors such as unreasonable management methods, poor afforestation quality, poor site conditions, extensive management, and improper tending. 2 The star anise forest;

[0088] Severely low-yielding star anise forests refer to forests with a canopy closure of <0.6, a fruit set rate of ≤70%, and a forest product yield of <750 kg / hm², caused by factors such as unreasonable management methods, poor afforestation quality, poor site conditions, extensive management, and improper tending. 2 The star anise forest;

[0089] The management and improvement of star anise forests includes four processes: land preparation, afforestation, tending, and harvesting.

[0090] Land preparation refers to the process of selecting and preparing land to provide a suitable environment for seedling growth; it mainly includes two modes: manual and mechanical.

[0091] Artificial land preparation refers to the process of selecting suitable land for tree growth through a series of manual operations such as digging pits and selecting sites.

[0092] Mechanical land preparation refers to the use of chemical reagents, burning, and other methods;

[0093] Afforestation, whether through seedling planting or sprouting, is the process of creating or renewing forests. It mainly includes three models: intensive plantation cultivation, existing forest improvement and cultivation, and tending of young and middle-aged forests.

[0094] Intensive plantation cultivation refers to the use of modern technology and management methods to efficiently plant artificial forests on a relatively small area in order to maximize economic and environmental benefits; its characteristics are: advocating mixed coniferous and broad-leaved trees, and mixed broad-leaved trees, with the proportion of mixed trees as the main tree species not exceeding 70%;

[0095] Existing forest improvement and cultivation refers to the activities of adjusting forest stand structure and tree species composition to form high-quality, high-yield, and high-efficiency forests with reasonable density, rich species, and complete functions, through artificial measures such as forest stand tending, replanting, and reseeding; for existing forest stands with good site conditions but unsuitable tree species, unclear target tree species, and unfulfilled production potential, transformation and cultivation are carried out through measures such as understory afforestation, thinning, and replanting to improve forest quality;

[0096] Young and middle-aged forest tending refers to the general term for various forest management measures taken from young forests and middle-aged forests to closed-canopy forests and mature stands, based on the cultivation objectives. It includes tending measures such as thinning, pruning, clearing shrubs and weeding, and fertilization. Its characteristics include: removing inferior trees and retaining superior ones, adjusting tree species structure and stand density, and improving forest growth conditions;

[0097] The term "tending" refers to the process of maintaining or increasing the ecological, social, and economic benefits of trees by taking measures such as thinning and fertilizing. There are four main models: near-natural, extensive, intensive, and precision quality improvement.

[0098] Near-natural tending refers to forest tending activities that follow the laws of forest succession, imitate natural forces, and minimize vegetation disturbance.

[0099] Extensive tending refers to a production method that prioritizes the natural environment, employs traditional techniques and methods, emphasizes cultivated area and labor quantity, and neglects input-output ratio and production efficiency.

[0100] Intensive tending refers to a management method that involves investing more means of production and labor in the same area for intensive cultivation, thereby increasing the total output by increasing the yield per unit area.

[0101] Precision quality improvement tending refers to a measure that uses tending measures such as clearing shrubs and weeding to strictly control the amount of logging, strictly control the specifications of seedlings, standardize land preparation, dig pits and other technical measures to improve the survival rate and quality of trees.

[0102] The harvesting refers to a series of processes, including picking and processing of forest products by hand, mainly including two modes: manual harvesting and functional evaluation.

[0103] Manual harvesting refers to the picking of star anise by hand after it has ripened and fallen to the ground, or by climbing the tree to pick it. It is strictly forbidden to use bamboo poles to knock or shake the branches or break the fruit branches.

[0104] There are two types of fruit processing: ① Blanching and drying: First, put fresh star anise into boiling water and stir with a long stick for 5 to 10 minutes. When the color of the star anise turns from green to light yellow, take it out and spread it on a drying ground or straw mat. Dry it in the sun for 4 to 5 days to get the finished product; ② Direct drying: Spread fresh star anise directly on a drying ground or straw mat and dry it in the sun for 4 to 5 days to get the finished product.

[0105] like Figures 2-6 The afforestation stage mainly includes land preparation, afforestation, tending, and harvesting:

[0106] Non-inefficient and low-yield star anise forests generally adopt measures such as land preparation, afforestation, tending of newly planted forests, tending of young and middle-aged forests, and tending of mature forests.

[0107] Inefficient star anise forests are generally managed through measures such as land preparation, afforestation, tending of newly planted forests, tending of young and middle-aged forests, tending of mature forests, replanting and reforestation, tending and harvesting, adjustment of tree species and replacement and transformation.

[0108] For mildly low-yielding star anise forests, measures such as land preparation, afforestation, tending of newly planted forests, tending of young and middle-aged forests, tending of mature forests, and replanting and reforestation are generally adopted.

[0109] Moderately low-yielding star anise forests generally adopt measures such as land preparation, afforestation, tending of newly planted forests, tending of young and middle-aged forests, tending of mature forests, replanting and reforestation, mixed planting, variety improvement and soil improvement;

[0110] Severely low-yielding star anise forests generally adopt measures such as land preparation, afforestation, tending of newly planted forests, tending of young and middle-aged forests, tending of mature forests, replanting and reforestation, mixed planting, variety improvement, forest stand transformation and soil improvement;

[0111] This invention relates to a carbon emission accounting method for the management and low-efficiency transformation of star anise forests. This method quantifies and assesses all material inputs, energy consumption, GHG emissions, etc., of all management processes and process unit activities during the management and operation of star anise forests, as well as during the transformation of inefficient star anise forests. The results are then converted into total CO2e emissions or emissions per unit functional unit.

[0112] E total =ΣG ij ×EF ij

[0113] Where Etotal is the total emissions (CO2e / yr / FU), i is the process involved in star anise forest management, j is the energy consumed in the production, equipment, machinery, and transportation of materials used in star anise management, G is the input amount, and EF is the emission factor, i.e., the emission intensity per unit input amount.

[0114] The goal is to generate the first detailed list of different management models for star anise in economic forests:

[0115] Different management models refer to production systems for producing high-yield and high-quality star anise, which are dedicated to forest management, forestry science and technology research and promotion, and application.

[0116] Activities refer to the material input and energy consumption trajectories of all production process units in all production functional areas;

[0117] Material input and distribution refers to the quantitative analysis of the use of resources and energy and the discharge of waste into the environment throughout the entire life cycle of a research system, such as a product, process, or activity.

[0118] GHG emissions refer to the total amount of greenhouse gas emissions caused by the consumption of environmental resources and energy;

[0119] Analysis refers to product carbon footprint analysis, that is, the climate impact of a single product, including all greenhouse gas emissions throughout the entire value chain; including life cycle inventory construction (LCI) and life cycle assessment (LCA):

[0120] A functional unit (FU) is 1 hectare of star anise forest land;

[0121] The scope of the system boundary (SB) analysis includes a greenhouse gas inventory from seedling planting to product harvesting, including material and chemical inputs, electricity and fuel use, and inputs and product transportation consumption of the nursery and its suppliers.

[0122] A life cycle inventory (LCI) refers to all inputs from seedling planting to product harvest, including inputs such as energy, materials, fertilizers, and chemicals.

[0123] In this invention, when the technical framework is determined, it includes:

[0124] The scope of the target defines the purpose and focus of carbon emission accounting, including the specific targets, expected results and application scenarios of the accounting; the target of this patent is to reduce carbon emissions from star anise forests, improve carbon sink capacity or evaluate the carbon benefits of low-carbon transformation measures;

[0125] The functional unit is a benchmark unit used for comparison and quantification; it provides a reference point to ensure the comparability of accounting results. In the star anise forest management and production sub-stage, the functional unit is carbon emissions per hectare of forest land.

[0126] The system boundary defines all inputs and outputs considered in the accounting process. It includes all stages of the entire life cycle, from raw material acquisition, production, transportation, use to disposal. For star anise forests, ① the boundary of a non-inefficient and low-yield star anise forest system includes afforestation, tending, and harvesting processes; ② the boundary of an inefficient and low-yield star anise forest system includes land preparation, afforestation, and tending processes.

[0127] In this invention, S2. Data list collection includes:

[0128] 2.1 The raw data includes the input and consumption of production materials, energy consumption, nursery time and material transportation; these were obtained through discussions with workers and managers of Paiyangshan Nursery, as well as from the archival records of star anise production; among them, sales lists and financial statements are important sources of input data.

[0129] 2.2 Information on upstream transportation is typically gathered through discussions with sales representatives; secondly, secondary data such as emission factors are obtained through a review of published literature and local standards.

[0130] 2.3 The tertiary data, which serves as supplementary information, comes from the National Bureau of Statistics.

[0131] In this invention, S3. Lifecycle inventory construction includes:

[0132] On-site energy usage: Based on interviews with nursery drivers and fertilizer distributors, the transportation vehicles and distances required for the management of star anise forests were summarized. Short-distance transportation was carried out using the nursery's own pickup trucks and rear-wheel-drive agricultural trucks (Fuda Climbing King). Interviews with drivers revealed that the pickup trucks consumed 15 liters / 100km, and the rear-wheel-drive trucks consumed 20 liters / 100km. Long-distance transportation relied on heavy-duty trucks (consuming 33L / 100km) to deliver fertilizers, pesticides, and substrates to distributors, who then further distributed them to various nurseries and forest farms. Distribution was carried out using the nursery's medium-sized rear-wheel-drive trucks. Interviews revealed that materials with low demand, such as shade nets, weed control fabric, plastic film, wire, carbendazim, and quicklime, were purchased together and transported to the nursery in single trips by truck. Therefore, the transportation of these materials should be combined into single trips.

[0133] The matrix consists of red clay and coconut fiber, which is considered a low-value byproduct of other production processes; neither requires extensive processing before it is used in the matrix; coconut fiber is a byproduct of coconut processing.

[0134] Synthetic fertilizers and chemicals used in production: Compound fertilizers are used in the production process, and the emissions from the production of these fertilizers are derived from the China Product Life Cycle Greenhouse Gas Emission Coefficient Database (CPCD); the transportation distance and truck type (engine size and brand) are provided by the supplier;

[0135] Consumables used in production: Non-woven containers and polypropylene pallets are used extensively in seedling production and are purchased from suppliers located 25 kilometers from the nursery. They are transported to the nursery by medium-sized trucks. The supplier provided data on the total transportation distance and the fuel economy of the medium-sized trucks.

[0136] Soil emissions: This list also considers emissions from substrates and natural soils, but only nitrous oxide emissions from fertilized soils; carbon dioxide emissions from the decomposition of organic matter used in potting mixes are not within the scope of this invention.

[0137] In this invention, S4. carbon footprint modeling includes:

[0138] i. General formula for calculating the carbon footprint of star anise: CF ST =CF seeding +CF tree ,

[0139] CFST represents the carbon footprint of star anise forest (kgCO2e / hm). 2 );

[0140] CFseedling represents the carbon footprint (kg CO2e / plant) during the production stage of star anise seedlings;

[0141] CFtree represents the carbon footprint (kgCO2e / hm) of the octagonal management stage. 2 );

[0142] ii. Carbon footprint calculation method: E=∑i(G i ×EF ij )

[0143] Where E represents the greenhouse gas emissions (kgCO2e) of functional unit j;

[0144] Gi represents the activity usage data of input i (kg / kWh / m³). 3 );EF ij The emission factor (kgCO2e / kg) of input i for greenhouse gas j;

[0145] CF=∑i(E i ×GWP j )

[0146] Wherein, CF represents the functional unit carbon footprint (kgCO2e);

[0147] E j Greenhouse gas emissions (kgCO2e) for functional unit j;

[0148] GWP j The global warming potential is a coefficient that correlates the effect of a unit mass of greenhouse gas j in a given internal radiation intensity with the effect of an equal amount of CO2 radiation.

[0149] iii. Electricity Carbon Footprint: EE lec =GE lec ×EFE lec

[0150] Among them, EE lec CO2 emissions (kgCO2 / FU) corresponding to the electricity used by the functional unit;

[0151] GE lec Power consumption (kWh) for functional units;

[0152] EFE lec Emissions from electricity production (kgCO2 / kWh);

[0153] IV. Carbon footprint of fuel:

[0154] Among them, EF uel CO2 emissions (kgCO2 / FU) corresponding to the use of fuel for the functional unit;

[0155] U maFuel consumption a per unit time of machine m, expressed in liters per hour (L / h); ef m Let m be the energy conversion efficiency of machine m;

[0156] T m The working time of machine m is expressed in hours (h).

[0157] EF a The emission factor of greenhouse gases produced by fuel a, expressed in kilograms of carbon dioxide equivalent per kilojoule (kgCO2e / KJ);

[0158] Q a The lower heating value of fuel a is expressed in kilojoules per liter (kJ / L).

[0159] v Fertilizer carbon footprint: E fer =∑i(A x ×M x ×f x ×EF x )

[0160] Where Efer is the CO2 emission (kgCO2 / FU) corresponding to the use of fertilizer in the functional unit;

[0161] A x The area where fertilizer x is applied is expressed in hectares (km²). 2 );

[0162] M x The amount of fertilizer applied per unit area in a single application is expressed as kg / ha / application.

[0163] f x This represents the average number of times fertilizer x is applied, expressed in times.

[0164] EF x The greenhouse gas emission factor of fertilizer x is expressed in kgCO2e / kg.

[0165] vi Chemical carbon footprint: E cha =∑i(AD) x ×EF x )

[0166] Among them, E cha CO2 emissions (kgCO2 / FU) corresponding to the use of chemicals in a functional unit;

[0167] ADx represents usage activity data for chemical x, in kg.

[0168] EFx represents the greenhouse gas emission factor of chemical x, expressed in kgCO2e / kg.

[0169] vii Material carbon footprint: E mat =∑i(AD) x ×EF x )

[0170] Among them, E mat CO2 emissions corresponding to the materials used in the functional unit

[0171] (kgCO2 / FU);

[0172] ADx represents the usage activity data for material x, in kg.

[0173] EFx is the greenhouse gas emission factor generated by material x, expressed in kgCO2e / kg;

[0174] viii Transportation carbon footprint: E T =M T ×G T ×EF T

[0175] Among them, E T CO2 emissions corresponding to the electricity used by the functional unit

[0176] (kgCO2 / FU);

[0177] G T The weight (t) of the transport functional unit;

[0178] G T Transport distance for functional units (km);

[0179] EF T Emissions from transportation production (kgCO2e / (t·km))

[0180] In this invention, S5. Carbon emission calculation: The carbon footprint is calculated using a model based on different emission factors and corresponding formulas;

[0181] In this invention, the following formula is used for sensitivity analysis in step S6:

[0182] E i =△A i / △F i

[0183] Ei represents the sensitivity coefficient of the input variable Fi;

[0184] ΔF i This represents the rate of change of the input variable F, which is 1% in this paper.

[0185] ΔA i This indicates that when the input variable F i The rate of change is ΔFi When the evaluation result A changes, the percentage change is expressed as %.

[0186] |E| represents the evaluation result A. i For input variable F i Sensitivity to change;

[0187] The larger the value of |E|, the better the evaluation result. i For input variable F i The more sensitive the variable, the greater its influence on the analysis results during the data analysis and evaluation process;

[0188] The specific steps are as follows:

[0189] 6.1 List all variables required for calculation: For each stage of the entire life cycle of star anise production, such as planting, harvesting, processing and transportation, list in detail the data of various activities that may cause carbon emissions and their corresponding emission factors; these activities may include, but are not limited to, land preparation, fertilizer input, pesticide use, consumption of machinery and equipment, and product transportation, and each activity may generate a different carbon footprint.

[0190] 6.2 Sensitivity Coefficient Calculation: For each activity, increase the activity level or input by 1%, then calculate the resulting carbon emissions, and finally use the ratio of this change to the total carbon emissions as the sensitivity coefficient; in this way, we can obtain an estimate of the degree of impact of various activities on the total carbon footprint.

[0191] 6.3 Result Comparison: After obtaining the sensitivity coefficients of all activities, we can compare the magnitude of these sensitivity coefficients. We generally believe that activities with large sensitivity coefficients are those that have a greater impact on carbon footprint, which are the factors we should focus on when optimizing production processes and reducing carbon emissions.

[0192] In this invention, S7. Data quality analysis includes: data can be categorized into primary and secondary data based on its source; within the system boundary of the octagonal production lifecycle assessment, each input carbon emission activity is analyzed; based on the main data obtained from sensitivity analysis, activities worthy of further detailed analysis are identified for data quality analysis; further data quality analysis is conducted on the above main data, with the specific steps as follows:

[0193] 7.1 Identifying and Determining Key Data: Based on the values ​​of each indicator in the carbon footprint assessment data quality indicator table, identify and determine the key data used in carbon emission accounting. Key data includes:

[0194] Data on the input and consumption of production materials (such as the amount and type of fertilizers, chemicals, and other materials used).

[0195] Energy consumption data (such as electricity consumption and fuel consumption).

[0196] Nursery time and material transportation data (such as means of transport, transport distance and transport frequency).

[0197] Greenhouse gas emission factor data related to the above inputs (such as emission factors for fertilizers, chemicals, and materials).

[0198] On-site energy usage data (such as fuel consumption data for transportation vehicles).

[0199] 7.2 Data Feature Analysis and Matrix Construction: Feature analysis is performed on the main data, including time span, geographical scope, and data source. Through analysis and judgment, the characteristics of each main data point are formed into a 1×5 index matrix to evaluate data quality. For example, the reliability of the data source, the coverage of the time span, and the geographical applicability are converted into specific numerical values ​​(such as scores from 1 to 5).

[0200] 7.3 Calculating Data Quality Scores: After determining the index values ​​for each key data point, a numerical quality index matrix is ​​obtained. Then, the mean and relative standard deviation of each data quality index relative to the highest index assigned a value of 5 are calculated to obtain the data quality scores for the primary activity level data and the carbon emission coefficient data.

[0201] 7.4 Interpretation and Application of Data Quality Analysis Results: Based on the data quality score, determine the reliability and applicability of the data. If the data score is high (e.g., the average value is above 4 and the relative standard deviation is low), the data quality is considered good and can be used in the carbon emission accounting process; if the score is low, further verification or supplementary data is needed to improve the accuracy of carbon emission accounting.

[0202] 7.5 Specific Example of Data Quality Analysis: For example, in the analysis of chemical usage data during the management of star anise forests, the annual usage data is first determined as the time span, the geographical scope as a specific region in Guangxi, and the data sources as field surveys and local statistical data. Then, these characteristics are transformed into a 1×5 data quality index matrix, and its mean and relative standard deviation are calculated to obtain the final quality assessment results of the chemical usage data.

[0203] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, substitutions, or variations made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for carbon footprint modeling in the management and low-carbon transformation of star anise forests, characterized in that, Includes the following steps: General formula for calculating the carbon footprint of star anise forest: CF ST =CF seeding +CF tree , Among them CF ST Carbon footprint of star anise forest; CF seedlin g represents the carbon footprint of star anise seedling production; CF tree Carbon footprint of Octagon during its operation; Carbon footprint calculation formula: E=∑i(G i ×EF ij ) Where E represents the greenhouse gas emissions of functional unit j; G i Data on the activity usage of input i; EF ij The emission factor of input i for greenhouse gas j; CF=∑i(E i ×GWP j ) Where CF represents the carbon footprint of the functional unit; E j denoted as functional unit j, where GWPj is the global warming potential value, which is the coefficient that correlates the effect of a unit mass of greenhouse gas j at a given internal radiation intensity with the effect of an equal amount of CO2 radiation. Carbon footprint of electricity: E Elec =G Elec ×EF Elec Among them, E Elec CO2 emissions corresponding to the use of electricity by the functional unit; G Elec Power consumption for functional units; EF Elec Emissions factors in electricity production; Carbon footprint of fuel: Among them, E Fuel CO2 emissions corresponding to the use of fuel by the functional unit; U ma Let f be the fuel consumption of machine m per unit time; ef m T represents the energy conversion efficiency of machine m. m EF is the working time of machine m. a The emission factor of greenhouse gases produced by fuel a; Q a The average lower heating value of fuel a; Fertilizer carbon footprint: E fer =∑i(A x ×M x ×f x ×EF x ) Among them, E fer CO2 emissions corresponding to the use of fertilizer by the functional unit; A x M represents the area where fertilizer x is applied; x f represents the amount of fertilizer applied per unit area in a single application; x EF represents the average number of fertilizations for fertilizer x; x Fertilizer x is a greenhouse gas emission factor; Chemical carbon footprint: E cha =∑i(AD) x ×EF x ) Among them, E cha CO2 emissions corresponding to the use of chemicals in the functional unit; AD x Data on the use of chemical x; EF x The greenhouse gas emission factor for chemical x; Material carbon footprint: E mat =∑i(AD) x ×EF x ) Among them, E mat The CO2 emissions corresponding to the materials used in the functional unit; AD x For the usage activity data of material x; EF x The emission factor of greenhouse gases generated by material x; Transportation carbon footprint: E T =M T ×G T ×EF T Among them, E T CO2 emissions corresponding to the electricity used by the functional unit; G T For the weight of the transport functional unit; G T For the transport distance of the functional unit; EF T Emissions from transportation production.

2. A method for carbon emission accounting in the management and low-carbon transformation of star anise forests, characterized in that, Including the carbon footprint modeling as described in claim 1, the method further includes the following steps: S1. Classify star anise forests according to soil fertility level, yield, etc.; determine the target range, functional units, and system boundaries; S2. Data list collection; S3. Lifecycle inventory construction; S5. Carbon emission calculation; S6. Sensitivity Analysis; S7. Data quality analysis; The sensitivity analysis uses the following formula: AND i =△A i / △F i E i This represents the input variable F. i Sensitivity coefficient; ΔF i This represents the rate of change of the input variable F; ΔA i This indicates that when the input variable F i The rate of change is ΔF i At that time, the rate of change of evaluation result A; |E| represents the evaluation result A. i For input variable F i Sensitivity to change; It includes the following steps: listing the variables required for the calculation, calculating the sensitivity coefficient, and comparing the results.

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