Carbon emission accounting method in illicium verum forest management and low-level 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

Technical Field

[0001] The present invention belongs to the field of forestry, and specifically relates to a carbon emission accounting method for star anise forest management and low-carbon transformation processes. Background Art

[0002] Star anise (Illicium verum Hook.f.) is an evergreen broad-leaved tree of 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 common spice in cooking.

[0003] Star anise forest is a general term for star anise plantations and star anise public welfare forests. Non-low-efficiency and low-yield star anise forests refer to those that achieve a yield of 750kg / hm2, a canopy density between 0.5 and 0.8, and no obvious pests and diseases through reasonable tending management, fertilization management and other management methods. However, due to the low price of star anise in the early days, the masses did not think about management, the planting density was too large, the star anise forest was overgrown with weeds, and there was a lack of investigation and detection of star anise pests and diseases, resulting in the star anise forest yield from about 10kg / plant with fruit to 2-3kg / plant. Based on this, it is particularly important to carry out low-quality transformation of star anise forests. Low-quality transformation refers to the implementation of supplementary forestry management activities for low-quality and low-efficiency forest stands through forestry engineering measures such as variety improvement, forest phase transformation and soil improvement, so as to restore to normal levels, increase production and income, and improve quality and efficiency.

[0004] Carbon emissions, the release of greenhouse gases into the atmosphere as a result of human activities, are one of the key drivers of global climate change.

[0005] Controlling carbon emissions from economic forests is crucial to achieving sustainable economic and environmental development. For example, CN116349539 A discloses a management method for star anise forests to protect flowers, promote fruit production and increase yield. Management and protection are carried out in star anise forests. Different measures are selected according to the different characteristics of star anise growth at different stages to improve the yield and quality of star anise forests. Lu Daotiao et al. proposed the star anise forest resources and management strategies in Liuwanshan District, Guangxi, classified the star anise forests and carried out relevant practical operation measures to improve the comprehensive benefits of forestry and promote sustainable economic development. There are endless studies on carbon emissions. For example, CN116303799A discloses a forestry carbon emission accounting method based on blockchain, which first determines the life cycle range 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 forestry industry in Jiangsu based on the Logistic model. However, the current accounting of forest carbon emissions is mainly focused on the macro level of the entire forestry industry, and often ignores the detailed consideration of specific forest species. Although this general accounting method can provide a rough overview of carbon emissions, it cannot accurately reflect the specific role and contribution of different forest types in the carbon cycle. Taking the octagonal forest as an example, it is necessary to conduct detailed classification based on factors such as its site conditions and production level. On this basis, a customized and appropriate management strategy can be proposed for each type of octagonal forest, and its production efficiency can be improved through improvement measures. At the same time, a detailed accounting of carbon emissions is carried out for each management model to ensure that management measures can not only promote the sustainable development of the octagonal forest, but also maximize its function as a carbon sink.

[0006] Carbon emission accounting is a key step in assessing and monitoring carbon emissions. It not only helps to understand the current carbon emission status, but also predicts future carbon emission trends.

[0007] The carbon emission accounting system boundary refers to the process of quantifying the greenhouse gases directly and indirectly generated by activities in the life cycle of forest products from seeds to products. This paper develops a detailed carbon emission accounting process for different forest types, management processes and technical processes to ensure that the carbon emissions of star anise forest management and low-carbon transformation models are accurately evaluated.

[0008] The product carbon footprint covers the carbon emissions of forest products throughout their life cycle, from seed development to product use and disposal. This paper focuses on the emission process of the life cycle from seedlings to product harvesting, including forest stand management and production, product processing and use. The forest stand management and production stage is particularly critical, and it is necessary to comprehensively consider factors such as tree species selection, management methods, natural factors and management and operation.

[0009] In summary: This paper aims to quantitatively evaluate carbon emissions from different management processes, forest types and yield quality classifications of octagonal forests, in order to provide a scientific basis for the sustainable development of octagonal forests. Summary of the invention

[0010] In order to solve the above technical problems, the present invention provides a method for calculating carbon emissions from the management and low-carbon transformation of star anise forests to solve the problems in the prior art. The technical solution adopted by the present invention is:

[0011] 1. A method for calculating carbon emissions from the management and low-carbon transformation of anise forests, characterized in that it comprises the following steps:

[0012] S1. Classify the octagonal forest according to soil fertility level, yield, etc.; determine the target range, functional unit and system boundary;

[0013] S2. Data inventory collection;

[0014] S3. Construction of life cycle inventory;

[0015] S4. Carbon footprint modeling;

[0016] S5. Calculation of carbon emissions;

[0017] S6. Sensitivity analysis;

[0018] S7. Data quality analysis.

[0019] The present invention has the following beneficial effects:

[0020] (1) Camellia oil plays an important role in the economic forest industry and provides solid support for the national strategic development. The carbon footprint accounting of the camellia oil production process will help improve the sustainability, resource utilization efficiency and industrial competitiveness of the economic forest industry.

[0021] (1) Balanced and detailed operations to improve accuracy: The two subsystems of afforestation and virgin oil extraction were determined, and the operational processes in forest management and product production were considered. The process division was detailed and close to the actual operation, which balanced the accuracy and operability of carbon footprint accounting;

[0022] (3) Reflect the key aspects of the system and improve the application value: effectively reflect the key aspects of tea oil production, and comprehensively consider social and environmental impacts, including factors such as social transportation costs and ecosystem services, and make comprehensive analysis comprehensive and close to reality, so that carbon footprint accounting is more specific and operational, and can provide practical application value, providing accurate guidance and decision-making support for the sustainable development of the economic forest industry;

[0023] (4) Multiple emission data to improve the credibility of calculations: Based on actual evidence, the emission differences of different input items in tea oil production during production and transportation are fully considered, and the credibility of carbon footprint accounting is significantly improved by accurately capturing the impact of tea oil production on carbon footprint;

[0024] (5) Differentiated calculations to significantly improve accuracy: Taking into account the differences in the production process caused by different production management methods, the production methods are differentiated and calculated. Compared with simplifying them into general categories, the accuracy of carbon footprint accounting is significantly improved;

[0025] (6) Analyze emission proportions and accurately quantify sources: Through detailed analysis of emission proportions, it is possible to accurately divide and quantify emission sources in different activities or processes, providing more insightful and practical carbon footprint information for industrial decision-making and promoting the sustainable development of my country's forestry production;

[0026] (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 basis for the carbon footprint accounting of tea oil production, and providing more trustworthy support for industry decision-making;

[0027] (8) Clear presentation of key information, easy to understand and use: Comprehensively display key factors, including uncertainty, sensitivity and emission proportion, making it easier for practitioners to 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;

[0028] (9) Considering emissions from different operating methods and providing targeted suggestions for reducing carbon footprint: Taking into account the specific carbon emission factors of different operating methods, a more accurate calculation result is 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 suggestions for reducing carbon footprint;

[0029] (10) Focus on the introduction of new technologies and innovative solutions to carbon footprint challenges: Not only the improvement of existing technologies is considered, but also the introduction of new technologies is paid attention to meet the challenges of carbon footprint of tea oil production, such as advanced pesticide spraying technology (drone spraying), optimized traffic process (differences in emission factors of different road transportation processes), etc., 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;

[0030] (11) Combined with the changes in the energy system, it provides comprehensive carbon footprint information for the entire industrial chain: It brings significant advantages based on the existing databases and algorithms. By combining the changes in the energy system, it not only focuses on the 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. Comprehensive consideration of transportation, energy, supply chain, industrial chain and other aspects, on the basis of the existing database, it captures the sources of carbon emissions more comprehensively, and provides decision makers with more detailed and practical data. This systematic optimization not only provides a more accurate data basis for carbon footprint accounting, but also provides strong support for the development of the green economy and the construction of a sustainable industrial chain.

[0031] (12) Compared with the existing patents for life cycle assessment of agricultural and forestry products, the method for constructing a life cycle emission inventory of the present invention adopts a more detailed and comprehensive method; the 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, through in-depth analysis of the environmental costs of each stage, including energy, materials and emissions in the production process, the calculation of environmental costs is more accurate and comprehensive, which provides decision makers with more detailed economic and environmental information and contributes to more sustainable economic development; supply chain carbon emission accounting has an in-depth understanding of the carbon emissions of each link in the supply chain, from raw material procurement to final product delivery, and the emission data is more accurate, which helps to comprehensively optimize the supply chain and reduce the overall carbon footprint. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of the carbon footprint accounting method of tea oil production from afforestation to first oil pressing;

[0033] Figure 2 It is the flow chart of the afforestation subsystem;

[0034] Figure 3 It is the flow chart of the virgin oil subsystem;

[0035] Figure 4 It is the boundary map of the carbon footprint accounting system for tea oil production from afforestation to first oil pressing;

[0036] Figure 5 It is the boundary map of the afforestation subsystem;

[0037] Figure 6 It is the boundary diagram of the virgin oil subsystem. DETAILED DESCRIPTION

[0038] The following will be combined with the embodiments of the present invention Figure 1-Figure 6, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0039] like Figure 1 The present invention relates to a carbon emission accounting method for the management and low-carbon transformation of octagonal forests. The following steps are performed for the management modes of octagonal forests of different forest stands: S1: classify the octagonal forests according to soil fertility level, yield, etc.; determine the target range, 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. The method aims to provide an accurate and efficient carbon emission accounting system to promote the sustainable management of octagonal forests and the protection of the ecological environment.

[0040] Star anise forest management and low-yield transformation refers to a collection of artificial forestry activities to manage or transform star anise forests in a differentiated manner according to soil fertility and yield performance, including non-low-efficiency and low-yield star anise forests, low-efficiency star anise forests, mildly low-yield star anise forests, moderately low-yield star anise forests and severely low-yield star anise forests, among which:

[0041] Non-low-efficiency and low-yield star anise forest refers to star anise plantations or public welfare forests without any low soil fertility caused by human or natural factors, and its forest product output is ≥750kg / hm 2 , the canopy density is between 0.5 and 0.8, and there are no obvious diseases and insect pests, the tree species selection and management methods are reasonable, and the trees are suitable for the site;

[0042] Low-efficiency star anise forest refers to a star anise forest caused by human or natural factors, whose tree growth is significantly declining and whose functional benefits are low compared with star anise forests under the same environmental conditions. The artificial forest varieties are poor and the trees are aging, and the output of forest products has significantly decreased. The public welfare forest has high climate vulnerability, poor ability to resist natural disasters such as fire, wind disasters, and floods, and the forest is incomplete, the canopy density is less than 0.5, and the ecological function is low.

[0043] Mild low-yield star anise forest refers to a forest with unreasonable management methods, poor afforestation quality, poor site conditions, extensive management and improper tending, resulting in a canopy density greater than 0.8, a fruiting plant rate of ≥ 70% and a forest product yield of < 750kg / hm2. 2 The octagonal forest;

[0044] Moderately low-yield star anise forest refers to a forest with unreasonable management methods, poor afforestation quality, poor site conditions, extensive management and improper tending, with a canopy density of 0.6≤<0.8, a fruiting plant rate of 40≤<70% and a forest product yield of <750kg / hm2. 2 The octagonal forest;

[0045] Severely low-yield star anise forest refers to a forest with unreasonable management methods, poor afforestation quality, poor site conditions, extensive management and improper tending, resulting in a canopy density of less than 0.6, a fruiting plant rate of ≤70% and a forest product yield of <750kg / hm2. 2 The octagonal forest;

[0046] The management and low-carbon transformation of star anise forests includes four steps: land preparation, afforestation, tending and harvesting:

[0047] Land preparation refers to the process of artificially selecting and preparing the land to provide a suitable environment for the growth of seedlings; there are mainly two modes: manual and mechanical;

[0048] Artificial land preparation refers to the selection of land suitable for tree growth through a series of operations such as digging holes and selecting sites through manpower.

[0049] Mechanical land preparation refers to the use of chemical reagents, fire, etc.;

[0050] Afforestation is the process of creating or renewing forests by planting seedlings or sprouts. There are three main modes: intensive plantation, existing forest transformation and young forest cultivation:

[0051] Intensive plantation cultivation refers to the use of modern technology and management methods to efficiently plant plantations on a smaller area to maximize economic and environmental benefits; its characteristics are: advocating mixed planting of coniferous and broad-leaved trees, and mixed planting of broad-leaved trees, with the proportion of the main planted tree species not exceeding 70%;

[0052] Existing forest improvement and cultivation refers to the activities of taking artificial measures such as forest tending, replanting and reseeding to adjust the forest stand structure and tree species composition and form a high-quality, high-yield and high-efficiency forest with reasonable density, rich species and complete functions; for forest stands with good site conditions in existing forests, but not suitable for the site and trees, unclear target tree species and unfulfilled production potential, measures such as afforestation under the canopy, thinning transformation, replanting and reseeding are implemented to improve forest quality;

[0053] Young and middle-aged forest tending refers to the general term for various forestry measures taken according to the cultivation goals from young and middle-aged forests to closed forests and before the forest stands mature. It includes tending measures such as thinning, pruning, weeding, fertilization, etc. Its characteristics are: removing inferior and retaining superior, adjusting tree species structure and forest density, and improving forest growth conditions;

[0054] The above-mentioned tending refers to the process of taking measures such as thinning and fertilization to maintain or increase the ecological, social and economic benefits of trees. There are four main modes: near-natural, extensive, intensive and precise quality improvement.

[0055] Near-natural tending refers to forest tending activities that follow the laws of forest succession, imitate natural forces, and minimize vegetation disturbance;

[0056] Extensive cultivation refers to a production method that takes the natural environment as the main factor, adopts traditional technologies and methods, pays attention to the cultivated area and the number of laborers, and ignores the input-output ratio and production efficiency.

[0057] Intensive cultivation refers to a management method that uses more production materials and labor to cultivate the same area intensively, and increases the total output by increasing the yield per unit area.

[0058] Precision quality improvement tending refers to a measure to improve the survival rate and quality of trees by strictly controlling the amount of felling through tending measures such as mowing and weeding, strictly controlling the specifications of seedlings, standardizing land preparation, digging holes and other technical measures;

[0059] The harvesting refers to a series of processes such as manual picking and processing of forest products, which mainly include two modes: manual harvesting and functional evaluation;

[0060] Manual harvesting means that after the star anise matures and falls to the ground, it is picked up manually or picked from the tree manually. It is strictly forbidden to knock with bamboo poles, shake branches or break fruit branches.

[0061] There are two types of fruit processing: ① Drying: first put the fresh star anise in boiling water, stir with a long stick for 5 to 10 minutes, and take it out when the color of the star anise changes from green to light yellow, then spread it on a drying yard or straw mat, and dry it in the sun for 4 to 5 days to get the finished product; ② Direct drying: spread the fresh star anise directly on a drying yard or straw mat, and dry it in the sun for 4 to 5 days to get the finished product;

[0062] like Figure 2-Figure 6 , the afforestation stage mainly includes land preparation, afforestation, tending and harvesting processes:

[0063] Non-low-efficiency and low-yield octagonal forests generally adopt measures such as land preparation, afforestation, new forest tending, young and middle forest tending, and mature forest tending;

[0064] Low-efficiency octagonal forests generally adopt measures such as land preparation, afforestation, new forest tending, middle and young forest tending, mature forest tending, replanting and reforestation, tending and felling, adjustment of tree species and transformation, and replacement and transformation;

[0065] Mild low-yield octagonal forests generally adopt measures such as land preparation, afforestation, new forest tending, young and middle forest tending, mature forest tending, and replanting and reforestation;

[0066] The measures generally adopted for medium-low-yield octagonal forests include land preparation, afforestation, tending of new afforestation, tending of middle-aged and young forests, tending of mature forests, replanting and reforestation, mixed planting, variety improvement and soil improvement;

[0067] Severe low-yield star anise forests generally adopt measures such as land preparation, afforestation, new forest tending, middle and young forest tending, mature forest tending, replanting and replanting, mixed planting, variety improvement, forest phase transformation and soil improvement;

[0068] The carbon emission accounting method for the operation and low-efficiency transformation process of the octagonal forest of the present invention refers to a method for quantifying and evaluating all material inputs, energy consumption, GHG emissions, etc. of all operation processes and all process unit activities in the operation and management process of the octagonal forest, as well as when transforming the inefficient octagonal forest, and converting it into the total emission of CO2e or the emission per unit functional unit, that is,

[0069] E total =ΣG ij ×EF ij

[0070] Where E total is the total emission (CO2e / yr / FU), i is the process involved in the management of the star anise forest, j is the energy consumed by the production of materials or equipment, instruments, and transportation in the star anise management process, G is the input, and EF is the emission factor, that is, the emission intensity per unit input:

[0071] The aim is to generate a first detailed list of different management models for economic forest star anise:

[0072] Different management models refer to the production system for producing high-yield and high-quality star anise, which is committed to forest management, forestry science and technology research, promotion and application, etc.

[0073] Activities refer to the material input and energy consumption trajectories of all production process units in all production functional areas;

[0074] Material input and distribution refers to the quantification of resource and energy use and waste discharge to the environment during the entire life cycle of the research system such as a product, process or activity;

[0075] GHG emissions refer to the total amount of greenhouse gas emissions caused by the consumption of environmental resources and energy;

[0076] The analysis refers to the product carbon footprint analysis, that is, the climate impact of a single product, including all greenhouse gas emissions throughout the value chain; including the construction of a life cycle inventory (LCI) and a life cycle assessment (LCA):

[0077] Functional Unit (FU) is 1 hectare of octagonal forest land;

[0078] The scope of the System Boundary (SB) analysis includes the greenhouse gas inventory from seedling planting to product harvesting, including material and chemical inputs, electricity and fuel use, and input and product transportation consumption by the nursery and its suppliers;

[0079] The Life Cycle Inventory (LCI) refers to all inputs from seedling planting to product harvesting, including energy, materials, fertilizers, chemicals, etc.

[0080] In the present invention, when the technical framework is determined, it includes:

[0081] The target scope defines the purpose and focus of carbon emission accounting, including the specific objectives, expected results and application scenarios of the accounting; the goal of this patent is to reduce carbon emissions from octagonal forests, improve carbon sequestration capacity or evaluate the carbon benefits of low-carbon measures;

[0082] Functional unit is a benchmark unit used for comparison and quantification, which provides a reference point to ensure the comparability of accounting results. In the production sub-stage of octagonal forest management, the functional unit is the carbon emissions per hectare of forest land;

[0083] 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 the octagonal forest, ① the non-inefficient and low-yield octagonal forest system boundary includes afforestation, tending and harvesting processes; ② the inefficient and low-yield octagonal forest system boundary includes land preparation, afforestation and tending processes;

[0084] In the present invention, S2. Data list collection includes:

[0085] 2.1. The original data include input and consumption of production materials, energy consumption, nursery time and material transportation; these are obtained through discussions with workers and managers of Paiyangshan Nursery, as well as from archival records of star anise production; among them, sales lists and financial statements are important sources for obtaining input data;

[0086] 2.2. Information on upstream transport is usually collected through discussions with sales representatives; secondly, secondary data such as emission factors are obtained through a review of published literature and local standards.

[0087] 2.3. The third-level data as supplementary information comes from the National Bureau of Statistics.

[0088] In the present invention, S3. Life cycle inventory construction includes:

[0089] On-site energy use: Based on interviews with nursery drivers and fertilizer distributors, the transportation tools and transportation distances of materials needed for star anise forest management were summarized. Short-distance transportation tools are the nursery's own pickup trucks and rear-wheel drive agricultural trucks (Ford Mountain King). After interviews with drivers, it was learned that the fuel consumption of pickup trucks is 15 liters / 100 kilometers, and the fuel consumption of rear-wheel drive trucks is 20 liters / 100 kilometers; long-distance transportation relies on heavy trucks (fuel consumption is 33L / 100Km), the purpose of which is to deliver fertilizers, pesticides and substrates to distributors, and then further distribute them to various nurseries and forest farms. During distribution, they are transported by the nursery's medium-sized rear-wheel drive trucks. During the interviews, it was found that materials with low demand, such as shade nets, weed-proof cloth, plastic film, iron wire, carbendazim and quicklime, were purchased together and transported to the nursery in a single trip by trucks. Therefore, the above materials should be combined into a single trip when transported;

[0090] Substrate: The substrate consists of red heart soil and coir, which is considered a low-value byproduct of other production processes; neither requires extensive processing before use in the substrate; coir is a byproduct of coconut processing;

[0091] Synthetic fertilizers and chemicals used in production: compound fertilizers are used in the production process, and the production emissions of these fertilizers are from the China Product Life Cycle Greenhouse Gas Emission Coefficient Database (CPCD); transportation distance and truck type (engine size and brand) are provided by the supplier;

[0092] Consumables in production: Non-woven containers and polypropylene trays are used in large quantities in seedling production. They are purchased from a supplier 25 kilometers away from the nursery and transported to the nursery by medium-duty trucks. The supplier provided data on the total transportation distance and the fuel economy of the medium-duty trucks.

[0093] Soil emissions: This inventory also considers emissions from substrates and natural soils, but is limited to nitrous oxide emissions from fertilized soils; CO2 emissions from the decay of organic matter used in potting mixes are not within the scope of this system.

[0094] In the present invention, S4. Carbon footprint modeling includes:

[0095] i Overall formula for calculating the carbon footprint of octagonal forest: CF ST =CF seeding +CF tree ,

[0096] where CF ST Carbon footprint of octagonal forest (kgCO2e / hm 2 );

[0097] CF seedling is the carbon footprint of star anise seedling production stage (kgCO2e / plant);

[0098] CFtree Carbon footprint of star anise operation (kgCO2e / hm 2 );

[0099] ii Carbon footprint calculation method: E = ∑i(G i ×EF ij )

[0100] Where, E is the greenhouse gas emissions of functional unit j (kgCO2e);

[0101] Gi is the activity usage data of input i (kg / KWh / m3); EFij is the emission factor of input i for greenhouse gas j (EmissionFactor, kgCO2e / kg);

[0102] CF=∑i(E i ×GWP j )

[0103] Among them, CF is the carbon footprint of the functional unit (kgCO2e);

[0104] E j is the greenhouse gas emissions of functional unit j (kgCO2e);

[0105] GWP j is the global warming potential, which is the coefficient that relates the impact of a unit mass of greenhouse gas j at a given internal radiative intensity to the impact of an equal amount of CO2 radiative intensity;

[0106] iii Electricity carbon footprint: E Elec =G Elec ×EF Elec

[0107] Among them, E Elec CO2 emissions corresponding to the electricity used by the functional unit (kgCO2 /

[0108] FU);

[0109] G Elec The amount of electricity used for the functional unit (KWh);

[0110] EF Elec is the emission factor for electricity production (kgCO2 / KWh);

[0111] iv Fuel Carbon Footprint:

[0112] Among them, E Fuel CO2 emissions corresponding to the fuel used for the functional unit

[0113] (kgCO2 / FU);

[0114] U ma is the fuel consumption of machine m per unit time, in liters per hour (L / h); ef m is the energy conversion efficiency of machine m;

[0115] T m is the working time of machine m, in hours (h);

[0116] EF a is the greenhouse gas emission factor produced by fuel a, in kgCO2e / KJ;

[0117] Q a is the average lower calorific value of fuel a, in kilojoules per liter (KJ / L);

[0118] v Fertilizer carbon footprint: E fer =∑ i (A x ×M x ×f x ×EF x )

[0119] Among them, E fer CO2 emissions from fertilizer use for functional units

[0120] (kgCO2 / FU);

[0121] A x is the application area of ​​fertilizer x, in hectares (km 2 );

[0122] M x The amount of fertilizer x applied per unit area at a time, in kg / ha / time;

[0123] f x is the average number of fertilization times of fertilizer x, in times;

[0124] EF x is the greenhouse gas emission factor produced by fertilizer x, in (kgCO2e / kg);

[0125] vi Chemical Carbon Footprint: E cha =∑ i (AD x ×EF x )

[0126] Among them, E cha CO2 emissions corresponding to the use of chemicals for functional units

[0127] (kgCO2 / FU);

[0128] AD x is the usage activity data of chemical x, in kg;

[0129] EF x is the greenhouse gas emission factor of chemical x, in kgCO2e / kg

[0130] vii Material Carbon Footprint: E mat =∑ i (AD x ×EF x )

[0131] Among them, E mat CO2 emissions corresponding to the materials used by the functional unit

[0132] (kgCO2 / FU);

[0133] AD x is the usage activity data of material x, in kg;

[0134] EF x is the greenhouse gas emission factor generated by material x, in kgCO2e / kg;

[0135] viii Transport Carbon Footprint: E T =M T ×G T ×EF T

[0136] Among them, ET is the CO2 emissions corresponding to the electricity used by the functional unit

[0137] (kgCO2 / FU);

[0138] GT is the weight of the transport functional unit (t);

[0139] GT is the functional unit transportation distance (km);

[0140] EFT is the transport production emission factor (kgCO2e / (t·km))

[0141] In the present invention, S5. Carbon emission calculation: using a model to calculate its carbon footprint according to different emission factors and corresponding formulas;

[0142] In the present invention, S6. Sensitivity analysis uses the following formula:

[0143] E i =△A i / △F i

[0144] E i It represents the input variable Fi The sensitivity coefficient of

[0145] ΔF i It represents the rate of change of the input variable F, and the rate of change in this paper is taken as 1%;

[0146] ΔA i , which means that when the input variable F i The rate of change of ΔF i ; When , the change rate of the evaluation result A is expressed in %;

[0147] |E| represents the evaluation result A i For the input variable F i Sensitivity to change;

[0148] |E| When the value is larger, it indicates that the evaluation result is A i For the input variable F i The more sensitive, that is, in the process of data analysis and evaluation, the greater the influence of input variable F on the analysis results;

[0149] The specific steps are:

[0150] 6.1. List the variables required for calculation: For each link in each sub-stage of the star anise production life cycle, such as planting, harvesting, processing and transportation, list in detail the 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, machinery and equipment consumption, and product transportation, etc. Each activity may produce a different carbon footprint;

[0151] 6.2. Calculation of sensitivity coefficient: For each activity, increase the activity level or input by 1%, then calculate the resulting carbon emissions, and finally take the ratio of this change to the total carbon emissions as the sensitivity coefficient; in this way, we can get an estimate of the impact of various activities on the total carbon footprint;

[0152] 6.3. Result comparison: After obtaining the sensitivity coefficients of all activities, we can compare the sizes of these sensitivity coefficients; we usually believe that activities with large sensitivity coefficients have a greater impact on carbon footprint, which is the factor we should focus on when optimizing the production process and reducing carbon emissions.

[0153] In the present invention, S7. Data quality analysis includes: data can be divided into primary data and secondary data according to the data source; within the system boundary of the star anise production life cycle assessment, each input carbon emission activity item is sorted out, and according to the main data obtained by the sensitivity analysis, the activity items worthy of further detailed analysis are found for data quality analysis; further data quality analysis is performed on the above main data, and the specific steps are as follows:

[0154] 7.1 Identify and determine the main data: According to the values ​​of each indicator in the carbon footprint evaluation data quality indicator table, identify and determine the main data used in carbon emission accounting. The main data include:

[0155] Input and consumption data of production materials (such as the amount and type of fertilizers, chemicals, materials, etc. used).

[0156] Energy consumption data (such as electricity consumption and fuel usage).

[0157] Nursery time and material transportation data (such as transportation tools, transportation distance and transportation frequency).

[0158] Data on greenhouse gas emission factors associated with the above inputs (e.g. emission factors for fertilizers, chemicals, materials).

[0159] On-site energy usage data (such as fuel consumption data for transportation vehicles).

[0160] 7.2 Data feature analysis and matrix construction: Conduct feature analysis on the main data, including time span, geographical scope, data source, etc. Through analysis and judgment, the features of each main data are formed into a 1×5 indicator value matrix to evaluate the quality of the data. For example, the reliability of the data source, the coverage of the time span, and the regional applicability are converted into specific values ​​(such as scores from 1 to 5).

[0161] 7.3 Calculation of data quality score: After determining the index value of each major data, a numerical quality index table matrix is ​​obtained. Then, the mean and relative standard deviation of each data quality index compared with the highest index value of 5 are calculated to obtain the data quality score of the primary activity level data and carbon emission factor data.

[0162] 7.4 Interpretation and application of data quality analysis results: According to the data quality score, judge the reliability and applicability of the data. If the data score is high (such as the average value is above 4 and the relative standard deviation is low), the data quality is considered to be good and can be used in the carbon emission accounting process; if the score is low, further verification or supplementation of relevant data is required to improve the accuracy of carbon emission accounting.

[0163] 7.5 Specific example of data quality analysis: For example, in the analysis of chemical use data in the operation of star anise forest, the full-year use data is first determined as the time span, the geographical scope is a specific area in Guangxi, and the data source is field surveys and local statistical data. Then, these features are converted into a 1×5 data quality indicator matrix, and the mean and relative standard deviation are calculated to finally obtain the quality assessment results of the chemical use data.

[0164] The embodiments described above are only descriptions of the preferred modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations, modifications, and substitutions made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. The carbon emission accounting method for the management and low-carbon transformation of star anise forests is characterized by: The following steps are involved: S1. Classify the octagonal forest according to soil fertility level, yield, etc.; determine the target range, functional unit and system boundary; S2. Data inventory collection; S3. Construction of life cycle inventory; S4. Carbon footprint modeling; S5. Calculation of carbon emissions; S6. Sensitivity analysis; S7. Data quality analysis.

2. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1 is characterized in that: Star anise forests include: non-low-efficiency and low-yield star anise forests, low-efficiency star anise forests, slightly low-yield star anise forests, moderately low-yield star anise forests and severely low-yield star anise forests.

3. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1, characterized in that: The management and low-level conversion process of star anise forests includes: land preparation, afforestation, tending and harvesting.

4. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1, characterized in that: The technical framework is being determined: The targets range from reducing carbon emissions from octagonal forests, improving carbon sequestration capacity, or evaluating the carbon benefits of low-change measures; Functional units include carbon emissions per hectare of forest land; The system boundary includes all inputs and outputs of the accounting process.

5. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1, characterized in that: When collecting data, the inventory includes: The raw data included inputs and consumption of production materials, energy consumption, nursery time, and material transportation.

6. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1, characterized in that: When constructing a life cycle inventory, include: on-site energy use; The substrate includes red heart soil and coconut shell fiber; synthetic fertilizers and chemicals used in production; Consumables in production; Soil emissions.

7. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1, characterized in that: When modeling your carbon footprint, include: The total formula for calculating the carbon footprint of octagonal forest is: CF ST =CF seeding +CF tree , where CF ST is the carbon footprint of the octagonal forest; CF seedling Carbon footprint of star anise seedling production stage; CF tree The carbon footprint of the operation stage of Bajiao; Carbon footprint calculation formula: E = ∑i(G i ×EF ij ) Where E is the greenhouse gas emissions of functional unit j; Gi is the activity usage data of input i; EFij is the emission factor of input i for greenhouse gas j; CF=∑i(E i ×GWP j ) Among them, CF is the carbon footprint of the functional unit; E j is the greenhouse gas emissions of functional unit j; GWP j is the global warming potential, which is the coefficient that relates the impact of a unit mass of greenhouse gas j at a given internal radiative intensity to the impact of an equal amount of CO2 radiative intensity; Electricity Carbon Footprint: E Elec =G Elec ×EF Elec Among them, E Elec G is the CO2 emission corresponding to the electricity used by the functional unit; Elec Power used by functional units; EF Elec is the emission factor for electricity production; Fuel Carbon Footprint: Among them, E Fuel The CO2 emissions corresponding to the fuel used by the functional unit; U ma is the fuel consumption per unit time of machine m; ef m is the energy conversion efficiency of machine m; T m is the working time of machine m; EF a is the greenhouse gas emission factor produced by fuel a; Q a is the average low calorific value of fuel a; Fertilizer Carbon Footprint: E fer =∑ i (A x ×M x ×f x ×EF x ) Among them, E fer A is the CO2 emission corresponding to the use of fertilizer in the functional unit; x M is the application area of ​​fertilizer x; x f is the amount of fertilizer x applied per unit area at a time; x is the average number of fertilization times of fertilizer x; EF x The greenhouse gas emission factor for fertilizer x; Chemical Carbon Footprint: E cha =∑ i (AD x ×EF x ) Among them, E cha The amount of CO2 emissions corresponding to the use of chemicals in functional units; AD x EF is the usage activity data of chemical x; x greenhouse gas emission factor for chemical x; Material carbon footprint: E mat =∑ i (AD x ×EF x ) Among them, E mat The amount of CO2 emissions corresponding to the materials used by the functional unit; AD x EF is the usage activity data of material x; x is the greenhouse gas emission factor for material x; Transport Carbon Footprint: E T =M T ×G T ×EF T Among them, ET is the CO2 emissions corresponding to the electricity used by the functional unit; GT is the weight of the transport functional unit; GT is the transport distance of the functional unit; EFT is the transport production emission factor.

8. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1, characterized in that: The following formula is used for sensitivity analysis: AND i =△A i / △F i E i It represents the input variable F i The sensitivity coefficient of ΔF i It represents the rate of change of the input variable F; ΔA i , which means that when the input variable F i The rate of change of ΔF i When , the change rate of the evaluation result A accordingly; |E| represents the evaluation result A i For the input variable F i Sensitivity to change; And include the following steps: listing the variables required for calculation, calculating the sensitivity coefficient, and comparing the results.

9. The method for calculating carbon emissions from the management and low-carbon transformation of star anise forests according to claim 1, characterized in that: The data quality analysis includes the following steps: Identify and determine primary data; analyze data features and construct a matrix; calculate data quality scores; interpret and apply data quality analysis results; specific examples of data quality analysis.

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