Forest carbon sink insurance method based on climate index

Abstract

The invention discloses a forest carbon sink insurance method based on climate indexes. The forest carbon sink insurance method comprises the steps that a winter air temperature abnormal index, a summer air temperature abnormal index, a growing season rainfall abnormal index, a growing season double-month rainfall abnormal index and an annual sunlight abnormal index are designed; taking the climate index as an independent variable and the forest annual carbon sequestration amount as a dependent variable to construct a forest carbon sequestration amount-climate index model; and on the basis of a forest carbon sequestration loss rate calculated by a forest carbon sequestration quantity-climate index model, comprehensively considering historical occurrence probabilities of different grade values of climate indexes and risk extreme values of future climate changes, and determining insurance claim triggering critical conditions and insurance claim rules of the forest carbon sequestration climate index insurance model. The method has the advantages that forest carbon sink climate loss caused by abnormal climate conditions can be quickly and efficiently quantified only through observation data of a national basic weather station, the problems of man-made interference influence and model difference are solved, and the defect that data cannot be obtained through satellite remote sensing due to weather reasons is overcome.

Description

Technical Field

[0001] The present invention relates to the cross-technical field of climate change and green carbon finance, and in particular to a forest carbon sink insurance method based on climate index. Background Art

[0002] Forest carbon sink refers to the process by which forest vegetation absorbs and fixes atmospheric carbon dioxide. Forests absorb atmospheric carbon dioxide through photosynthesis and fix it in branches, leaves, trunks and soil roots. They are the most powerful carbon sinks in terrestrial natural ecosystems, and these carbon sinks can be traded as emission reductions in the carbon trading market. Operators and managers of carbon sink forests obtain income and financial support by selling forest carbon sinks in the carbon trading market. The financial income can be used to further promote the development of carbon sink forests, expand afforestation areas, improve afforestation quality, and strengthen forest ecological protection.

[0003] In the process of carbon trading, the carbon emission reduction that carbon sink forests can provide (i.e., the carbon emission quota purchased by carbon emission enterprises) is not a fixed value, but changes every year with the change of forest carbon sequestration capacity. At present, there are three main types of carbon financial insurance in China that are guaranteed by forest carbon sinks. The first type is the traditional insurance method of field investigation and loss assessment, that is, professional institutions or personnel measure the trees in the assessment area through sample plot survey methods, such as measuring the diameter, age, height and species of trees to assess the carbon sink changes (sometimes it involves cutting down the sample plots and then replanting them); the second type is forest carbon sink insurance designed based on satellite remote sensing index; the third type is forestry carbon sink insurance that sets a certain amount of trees or types in advance, and when they suffer from force majeure factors such as pests or forest fires, remote sensing or biomass models are used to estimate the amount of losses for insurance claims.

[0004] The above methods face some problems in the application of carbon financial insurance business, such as remote sensing index insurance, which is affected by different remote sensing assessment models, the complexity of forest ecosystems, tree age and species, and the reliability of satellite sensors (such as cloud cover, satellite flight attitude, etc.). The assessment results obtained are greatly different from the actual situation, especially when determining damages and claims, the recognition of the results is low, and it is even more inappropriate in the southern region of my country with rainy weather. The field survey damage assessment method (i.e., sample plot survey) is limited by the number, distribution and type of sample plots, and it is difficult to truly reflect the changes in forest carbon sinks in a large area. In addition, manual measurement operations are easily affected by operational errors and human factors, resulting in deviations. In addition, field surveys consume a lot of manpower and material resources, have a long time period, and poor timeliness. Forestry carbon sink insurance is a statistical calculation for the total or partial loss of the bidding object, which is difficult to reflect the impact of natural disasters on it, and is affected by disaster forecasts or disaster announcements. It is difficult to operate in practice. Summary of the invention

[0005] The purpose of the present invention is to provide a forest carbon sink insurance method based on climate index, so as to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A forest carbon sink insurance method based on climate index, comprising the following steps:

[0008] S1. Climate index design:

[0009] Design the winter temperature anomaly index to quantify the degree of abnormally low winter temperature, the summer temperature anomaly index to quantify the degree of abnormally high summer temperature, the growing season precipitation anomaly index to quantify the degree of abnormally high growing season precipitation, the growing season bimonthly precipitation anomaly index to quantify the degree of abnormally low bimonthly precipitation in the growing season, and the annual sunshine anomaly index to quantify the degree of abnormally low annual sunshine.

[0010] S2. Construction of forest carbon sequestration-climate index model:

[0011] The forest carbon sequestration-climate index model was constructed using the multivariate linear regression statistical method, with the climate index as the independent variable and the annual forest carbon sequestration as the dependent variable.

[0012] S3. Design of claim rules model for forest carbon sink climate index insurance:

[0013] Based on the forest carbon sink loss rate calculated by the forest carbon sequestration-climate index model, and taking into account the historical occurrence probability of different levels of the climate index and the extreme risk values ​​of future climate change, the critical conditions for triggering insurance claims and the insurance claims rules of the forest carbon sink climate index insurance model are determined.

[0014] Preferably, step S1 specifically includes the following contents:

[0015] S11. Calculate the winter climate anomaly index to quantify the degree of abnormally low winter temperature and use it to measure the forest carbon sink loss caused by low winter temperature; the calculation formula is:

[0016] WTAI=(TL-T 12 )+(TL-T1)+(TL-T2)

[0017] Among them, WTAI is the winter climate anomaly index; T 12 T1, T2 are the average temperatures measured by national basic meteorological stations in December, January and February of the following year respectively; TL is the low temperature critical value;

[0018] S12. Calculate the summer temperature anomaly index to quantify the degree of abnormally high summer temperature and use it to cross-reference the forest carbon sink loss caused by the high summer temperature. The calculation formula is:

[0019] STAI=(T6-T h )+(T7-T h )+(T8-T h )

[0020] Among them, STAI is the summer temperature anomaly index; T6, T7, and T8 are the monthly average temperatures measured by national basic meteorological stations in June, July, and August respectively; T h is the high temperature critical value;

[0021] S13. Calculate the growing season precipitation anomaly index to quantify the degree of abnormal precipitation in the growing season and use it to measure the forest carbon sink loss caused by excessive precipitation during the peak forest growth season; the calculation formula is,

[0022]

[0023]

[0024] Among them, GSPAI-S is the growing season precipitation anomaly index; SP i is the monthly precipitation measured by the national basic meteorological station in the i-th month, i = 3 to 11; SP h is the critical value of precipitation in a single month;

[0025] S14. Calculate the bimonthly precipitation anomaly index during the growing season to quantify the degree of bimonthly precipitation anomaly during the growing season and use it to measure the forest carbon sink loss caused by less precipitation during the peak forest growth season. The calculation formula is:

[0026]

[0027] Among them, GSPAI-B is the bimonthly precipitation anomaly index during the growing season; BP i is the bimonthly cumulative precipitation measured by the national basic meteorological station in the i-th month, i = 3-11; BPL is the critical value of bimonthly cumulative precipitation;

[0028] S15. Calculate the annual sunshine anomaly index to quantify the degree of annual sunshine anomaly and use it to measure the forest carbon sink loss caused by insufficient sunshine. The calculation formula is:

[0029]

[0030] Among them, S i is the monthly sunshine hours measured by the national basic meteorological station in the i-th month, i = 1~12; Sh is the critical value of the monthly sunshine hours.

[0031] Preferably, in step S2, the construction formula of the forest carbon sequestration-climate index model is:

[0032] NPP=B0-A1*(WTAI)-A2*(STAI)-A3*(GPAI-S)-A4*(GPAI-B)-A5*(YSAI)

[0033] Among them, NPP is the annual carbon sequestration of the forest; B0 is the regression constant term of the MLR equation; A1, A2, A3, A4, and A5 are the regression coefficients of the MLR equation corresponding to the WTAI, STAI, GPAI-S, GPAI-B, and YSAI climate index terms, respectively.

[0034] Preferably, MLR uses a stepwise regression method to test the introduction of climate indices WTAI, STAI, GPAI-S, GPAI-B and YSAI at a significance level of 0.05 to determine the final form of the regression equation.

[0035] Preferably, in step S3, the calculation formula for the forest carbon sink loss rate is:

[0036] LR k =(L k / L max-G100 )*100

[0037] Among them, LR k is the forest carbon sink loss rate at the kth level of the climate index; L k is the forest carbon sink loss of the kth level; L max-G100 This is the forest carbon sink loss corresponding to the once-in-a-century climate index value calculated by the Gumble extreme value model.

[0038] Preferably, in step S3, the critical conditions for triggering insurance claims are determined by setting the climate index value corresponding to the probability of occurrence of 75-85% as the critical conditions for triggering insurance claims based on the probability of occurrence of each level of the climate index in the past 50 years or more.

[0039] Preferably, before step S1, the method further includes: S01, calculating the forest carbon sequestration per unit area:

[0040] The forest net primary productivity under map projection is calculated based on digital elevation to obtain the spatial forest carbon sequestration per unit surface area of ​​the actual surface; the calculation formula is:

[0041]

[0042] Among them, S1 is the carbon sequestration amount in the spatial range; S n is the unit surface area of ​​the nth spatial grid unit, n=1~N, N is the total number of spatial grid units; C is the forest carbon sequestration per unit area.

[0043] Preferably, between S01 and S1, S02, biological correction of forest carbon fixation time series:

[0044] Based on the forest carbon sequestration per unit area in space, the biological trend of the natural growth of forest carbon sequestration capacity due to tree age increase implied in the forest carbon sequestration time series is eliminated to obtain the biologically corrected forest carbon sequestration time series; the calculation formula is:

[0045] DTNPP j =NPP j +(T m -T j )×b

[0046]

[0047] Among them, NPP j and DTNPP j are the forest carbon sequestration in the jth year before and after biological correction, respectively; T m is the year corresponding to the maximum annual value of forest carbon sequestration in the annual time series; T j is the jth year, j = 1~J, J is the total number of years; b is the linear trend rate of forest carbon sequestration; T f The last year in the annual time series; NPP MEAN is the mean NPP of all years in the annual time series.

[0048] Preferably, after step S3, the method further includes: S4, designing a forest carbon sink climate index insurance product:

[0049] Set up insurance types and protection contents, specifically including five product types: Type A for low temperature in winter, Type B for high temperature in summer, Type C for more precipitation during the growing season, Type D for less precipitation during the growing season, and Type E for fewer annual sunshine hours.

[0050] The beneficial effects of the present invention are as follows: by designing a climate index that can characterize the degree of abnormal climate conditions, a "Forest Carbon Sequestration-Climate Index" (FCS-CI) model is constructed, and a forest carbon sink insurance technology based on a climate index is developed, which realizes the rapid and efficient quantification of forest carbon sink climate losses caused by abnormal climate conditions with only the observation data of national basic meteorological stations, solves the problems of human interference and model differences, and makes up for the deficiency of satellite remote sensing in obtaining data due to weather reasons, thus providing an original technology for the customization of forest carbon sink insurance types and objective and efficient disaster loss assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of an insurance method in an embodiment of the present invention;

[0052] Figure 2It is a biologically revised schematic diagram of forest carbon sequestration in Kaihua County from 2000 to 2022 in an embodiment of the present invention;

[0053] Figure 3 1. It is a schematic diagram of comparative analysis between FCS-CI model simulation value and DTNPP value of Kaihua County in an embodiment of the present invention;

[0054] Figure 4 It is a schematic diagram of the calculation of forest carbon sink loss and insurance claims from 2000 to 2022 in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] Embodiment 1

[0057] Climate and climate change will directly or indirectly affect the carbon sequestration capacity of forest vegetation to a great extent, especially extreme weather and climate events such as high temperature and drought, low temperature and rain, snow and freezing, abnormal precipitation, and cloudy and rainy weather with little sunshine, which will have a significant impact on the carbon sequestration capacity of forest vegetation and the forest carbon sink function.

[0058] like Figure 1 As shown, in this embodiment, from the perspective of responding to climate change and protecting forest carbon sink climate losses, a climate index that can characterize the degree of abnormal climate conditions is designed, a "Forest Carbon Sequestration-Climate Index" (FCS-CI) model that can quantify the impact of climate anomalies on forest carbon sequestration is constructed, and a climate index insurance rule model that can be objectively and efficiently used for damage assessment and claims is developed, thereby providing a forest carbon sink insurance method based on a climate index. The method specifically includes six parts, as follows:

[0059] 1. Calculation of forest carbon sequestration per unit area

[0060] The forest net primary productivity under map projection is calculated based on digital elevation to obtain the spatial forest carbon sequestration per unit surface area of ​​the actual surface; the calculation formula is:

[0061]

[0062] Among them, S1 is the carbon sequestration amount in the spatial range; S n is the unit surface area of ​​the nth spatial grid unit, n=1~N, N is the total number of spatial grid units; C is the forest carbon sequestration per unit area.

[0063] 2. Biological correction of forest carbon sequestration time series

[0064] Biological correction of forest carbon sequestration time series refers to removing the biological trend of natural growth of forest carbon sequestration capacity due to tree age increase (from young forest to mature forest) implied in the forest carbon sequestration time series, and obtaining the biologically corrected forest carbon sequestration time series; the calculation formula is:

[0065] DTNPP j =NPP j +(T m -T j )×b

[0066]

[0067] Among them, NPP j and DTNPP j are the forest carbon sequestration in the jth year before and after biological correction, respectively; T m is the year corresponding to the maximum annual value of forest carbon sequestration in the annual time series. (For example, in the annual series of forest carbon sequestration from 2000 to 2022, the forest carbon sequestration in 2021 is the largest, so T m =2021); T j is the jth year, (e.g. 2022, then T j =2022), j = 1~J, J is the total number of years; when (T m -T j ) is negative, it is calculated as 0; b is the linear trend rate of forest carbon sequestration (i.e., forest net primary productivity NPP); T f The last year in the annual time series; NPP MEAN is the mean NPP of all years in the annual time series.

[0068] 3. Climate Index Design

[0069] Design the winter temperature anomaly index to quantify the degree of abnormally low winter temperature, the summer temperature anomaly index to quantify the degree of abnormally high summer temperature, the growing season precipitation anomaly index to quantify the degree of abnormally high growing season precipitation, the growing season bimonthly precipitation anomaly index to quantify the degree of abnormally low bimonthly precipitation in the growing season, and the annual sunshine anomaly index to quantify the degree of abnormally low annual sunshine. Specifically:

[0070] (1) Calculation of Winter Temperature Anomaly Index (WTAI), one of the climate indices. It is used to characterize the degree of abnormality of winter temperature (December to February of the following year) relative to the climate average. In winter, high temperatures are usually conducive to tree growth and will not cause forest carbon sink loss; but low temperatures often inhibit tree growth and may even cause trees to stop growing (such as forests in Northeast my country), reducing the forest's carbon fixation capacity or even stopping photosynthesis. In view of the correlation between forest carbon fixation and winter temperature, a formula is designed to calculate the winter climate anomaly index to quantify the degree of abnormally low winter temperature, which is then used to measure the forest carbon sink loss caused by low winter temperature; the calculation formula is,

[0071] WTAI=(TL-T 12 )+(TL-T1)+(TL-η2)

[0072] Among them, WTAI is the winter climate anomaly index; T 12 T1, T2 are the average temperatures measured by national basic meteorological stations in December, January and February of the following year respectively; TL is the low temperature critical value; when T 12 , T1, T2 are higher than TL, that is: (TL-T 12 When there are negative values ​​in the three sub-items (TL-T1), (TL-T2), the negative items will be counted as "0".

[0073] (2) Calculation of Summer Temperature Anomaly Index (STAI), one of the climate indices. It is used to characterize the degree of abnormality of summer (June to August) temperature relative to the climate average. High summer temperatures are usually not conducive to tree growth. High temperatures can cause water vapor pressure deficit in trees, limit photosynthesis, and thus reduce tree growth rate and carbon fixation capacity, resulting in forest carbon sink loss. Low summer temperatures usually not only do not inhibit tree growth, but promote tree growth, because low summer temperatures are more likely to be within the optimal temperature range for tree growth. In view of the correlation between forest carbon fixation and summer temperature, a formula is designed to calculate the summer temperature anomaly index to quantify the degree of abnormally high summer temperature, which is subsequently used to measure the forest carbon sink loss caused by high summer temperature. The calculation formula is,

[0074] STAI=(T6-T h )+(T7-T h )+(T8-T h )

[0075] Among them, STAI is the summer temperature anomaly index; T6, T7, and T8 are the monthly average temperatures measured by national basic meteorological stations in June, July, and August respectively; T his the high temperature critical value; when T6, T7, T8 are higher than T h , that is: (T6-T h )、(T7-T h )、(T8-T h )When any of the three sub-items have negative values, the negative items will be counted as “0”.

[0076] (3) One of the climate indices, the Growth Season Monthly Precipitation Anomaly Index (GSPAI-S), is calculated. It is used to characterize the degree of abnormality of monthly precipitation in the growing season (South my country: March to November) relative to the climate average. Under normal circumstances, excessive or insufficient precipitation will cause forest carbon sink losses. When precipitation is too low and soil moisture is insufficient, trees do not have enough water and energy to maintain the photosynthesis process. Normal photosynthesis is inhibited, the forest's ability to absorb carbon dioxide decreases, and its carbon sink function weakens. However, excessive precipitation and oversaturation of soil moisture will also inhibit tree growth and weaken the forest's carbon sequestration capacity. In view of the correlation between forest carbon sequestration and atmospheric precipitation, a formula is designed to calculate the growing season precipitation anomaly index to quantify the degree of abnormal excessive precipitation in the growing season, which will be used to measure the forest carbon sink loss caused by excessive precipitation during the growing season. The calculation formula is,

[0077]

[0078]

[0079] Among them, GSPAI-S is the growing season precipitation anomaly index; SP i is the monthly precipitation measured by the national basic meteorological station in the i-th month, i = 3 to 11; SP h It is the critical value of precipitation in a single month.

[0080] (4) Calculation of Growth Season Bimonthly Precipitation Anomaly Index (GSPAI-B), one of the climate indices. It is used to characterize the degree of abnormality of bimonthly precipitation in the growing season (March to November) relative to the climate average. A formula is designed to calculate the Growth Season Bimonthly Precipitation Anomaly Index to quantify the degree of abnormal bimonthly precipitation in the growing season. It is then used to measure the forest carbon sink loss caused by insufficient precipitation during the forest growing season. The calculation formula is:

[0081]

[0082] Among them, GSPAI-B is the bimonthly precipitation anomaly index during the growing season; BP iis the bimonthly cumulative precipitation measured by the national basic meteorological station in the i-th month, i = 3 to 11; BPL i is the critical value of bimonthly cumulative precipitation; when (BPL i -BP i ) is a negative value, it is evaluated as "0".

[0083] (5) Calculation of the Yearly Sunshine Anomaly Index (YSAI), one of the climate indices. It is used to characterize the degree of abnormality of annual sunshine hours relative to the climate average. A formula is designed to calculate the annual sunshine anomaly index to quantify the degree of abnormal lack of annual sunshine, which is then used to measure the forest carbon sink loss caused by lack of sunshine. The calculation formula is:

[0084]

[0085] Among them, S i Sh is the monthly sunshine hours measured by the national basic meteorological station in the i-th month, i = 1 to 12; i is the critical value of monthly sunshine hours; when (Sh i -S i ) is a negative value, it is evaluated as "0".

[0086] 4. Construction of forest carbon sequestration-climate index model

[0087] The construction of the "Forest Carbon Sequestration-Climate Index" model refers to the use of the Multiple Linear Regression (MLR) statistical method, with climate indices such as the Winter Temperature Anomaly Index (WTAI), Summer Temperature Anomaly Index (STAI), Growing Season Monthly Precipitation Anomaly Index (GSPAI-S), Growing Season Bimonthly Precipitation Anomaly Index (GSPAI-B) and Annual Sunshine Anomaly Index (YSAI) as independent variables, and forest carbon sequestration (NPP) as the dependent variable to construct the "Forest Carbon Sequestration-Climate Index" (FCS-CI) model. The construction formula of the FCS-CI model is:

[0088] NPP=B0-A1*(WTAI)-A2*(STAI)-A3*(GPAI-S)-A4*(GPAI-B)-A5*(YSAI)

[0089] Among them, NPP is the annual carbon sequestration of the forest; B0 is the regression constant of the MLR equation; A1, A2, A3, A4, and A5 are the MLR equation regression coefficients corresponding to the climate index terms of WTAI, STAI, GPAI-S, GPAI-B, and YSAI, respectively. MLR uses stepwise regression to test the introduction of climate indexes WTAI, STAI, GPAI-S, GPAI-B, and YSAI at a significance level of 0.05 to determine the final form of the regression equation.

[0090] V. Design of Claim Rules Model for Forest Carbon Sink Climate Index Insurance

[0091] The forest carbon sink climate index insurance claim rules model is based on the forest carbon sink loss rate (or loss value) calculated by the forest carbon sequestration-climate index model, and comprehensively considers the historical probability of occurrence of different levels of climate index (WTAI, STAI, GSPAI-S, GSPAI-B and YSAI) (data period: more than 50 years) and the extreme value of future climate change risk (calculated using the Gumble extreme value model) to determine the insurance claim triggering critical conditions and insurance claim rules of the forest carbon sink climate index insurance model. The claim rules are the forest carbon sink loss rates at different claim levels.

[0092] The calculation formula for forest carbon sink loss rate is:

[0093] LR k =(L k / L max-G100 )*100

[0094] Among them, LR k is the forest carbon sink loss rate at the kth level of the climate index (one of the five items: WTAI, STAI, GSPAI-S, GSPAI-B and YSAI); L k is the forest carbon sink loss of the kth level; L max-G100 This is the forest carbon sink loss corresponding to the once-in-a-century climate index value calculated by the Gumble extreme value model.

[0095] The critical conditions for triggering insurance claims are determined by setting the climate index value corresponding to the probability of occurrence of 75-85% as the critical conditions for triggering insurance claims based on the probability of occurrence of each level of the climate index in the past 50 years (or more than 50 years).

[0096] VI. Design of Forest Carbon Sink Climate Index Insurance Products

[0097] Set up insurance types and protection contents, specifically including five product types: low temperature in winter (insurance type A), high temperature in summer (insurance type B), more precipitation during the growing season (insurance type C), less precipitation during the growing season (insurance type D), and less annual sunshine hours (insurance type E).

[0098] In this embodiment, the forest carbon sink insurance technology method based on the climate index is compared with the traditional loss assessment insurance (forest carbon sink value insurance). The climate index insurance can save a lot of manpower, material resources and time costs, and the loss assessment and claims are simple, fast and efficient; compared with remote sensing index insurance (such as: forestry carbon sink index insurance, forest carbon sink remote sensing index insurance, forest carbon sink insurance, etc., the product names are different), the data source of climate index insurance loss assessment and claims is more objective, authoritative and reliable. The most basic data for remote sensing index insurance loss assessment and claims is satellite remote sensing detection data. Due to problems such as satellite operating attitude, cloud cover, geometric correction, etc., satellite detection data is sometimes less reliable (especially in the rainy areas in the south), and there may also be human intervention in the remote sensing inversion of forest carbon sinks. The measurement value of climate index insurance loss assessment and claims is the climate index, and its data comes from the data of meteorological observations carried out by the national basic meteorological observation stations in accordance with laws and regulations. The observation data is continuous, complete, objective, authoritative and reliable. The climate index insurance model for forest carbon sinks (CIIFCS) of the present invention is a regional model. Theoretically, as long as it is located in the same microclimate zone (within 100km*100km), the model can be used for insurance claims.

[0099] Embodiment 2

[0100] In this embodiment, the design of forest carbon sink climate index insurance product in Kaihua County, Quzhou City, Zhejiang Province is taken as an example to specifically illustrate the execution process of the method of the present invention.

[0101] 1. Calculate the amount of carbon sequestration per unit area of ​​space.

[0102] Based on the net primary productivity (NPP) of vegetation calculated by remote sensing vegetation photosynthesis model and forest autotrophic respiration model, the NPP spatial grid data obtained by the model was extracted according to the land use classification of Kaihua County, and then the spatial forest carbon sequestration per unit surface area of ​​the actual surface was obtained based on digital elevation calculation.

[0103] 2. Biological revision of forest carbon sequestration time series

[0104] Remove the biological trend of natural growth of forest carbon sequestration capacity due to forest age increase (from young forest to mature forest) implied in the Kaihua County forest carbon sequestration time series (2000-2022). m =2021,T j =2022, J=23. The results of biological correction of the time series of forest carbon sequestration in Kaihua County are as follows: Figure 2 .

[0105] 3. Climate Index Calculation

[0106] (1) Calculation of Winter Temperature Anomaly Index (WTAI). 12T1, T2 are the monthly average temperatures measured by the National Basic Meteorological Station (Kaihua Station) in December, January and February of the following year, respectively. TL is the low temperature critical value, which is 6°C. 12 , T1, T2 are higher than TL, that is: (TL-T 12 When the three sub-items (TL-T1), (TL-T2) have negative values, the negative items are counted as "0". The calculation results are shown in Table 1.

[0107] Table 1 Calculated values ​​of climate index in Kaihua County from 2000 to 2022

[0108] years WTAI(℃) STAI(℃) GPAI-S GPAI-B(mm) 2000 1.0 0.5 1 169.4 2001 0.0 0.5 2 52.9 2002 0.0 0.0 3 0.0 2003 1.7 2.3 3 199.9 2004 0.9 0.0 1 128.8 2005 4.4 0.0 1 377.8 2006 0.0 0.8 3 206.5 2007 0.6 0.7 0 135.6 2008 3.5 0.3 3 0.0 2009 1.5 0.0 1 116.9 2010 0.0 0.6 6 0.0 2011 4.0 0.9 1 29.5 2012 1.4 0.6 5 0.0 2013 2.1 3.0 2 353.3 2014 0.7 0.0 4 0.0 2015 0.4 0.0 4 0.0 2016 0.4 1.8 4 227.1 2017 0.0 0.8 3 41.3 2018 1.0 1.2 2 162.9 2019 0.0 0.5 4 144.0 2020 0.0 1.1 4 0.0 2021 0.3 0.0 3 51.3 2022 1.0 4.4 3 347.7

[0109] (2) Calculation of Summer Temperature Anomaly Index (STAI). T6, T7, and T8 are the monthly average temperatures measured at the National Basic Meteorological Station (Kaihua Station) in June, July, and August, respectively. h is the high temperature critical value. When T6, T7, T8 are higher than T h , that is: (T6-T h )、(T7-T h )、(T8-T h )When any of the three sub-items has a negative value, the negative value item is counted as "0". The calculation results are shown in Table 1.

[0110] (3) Calculation of growing season monthly precipitation anomaly index (GSPAI-S). i It is the monthly precipitation measured by the national basic meteorological station (Kaihua Station) from March to November. h It is the critical value of precipitation in a single month, which is 210mm.

[0111] (4) Calculation of the growing season bimonthly precipitation anomaly index (GSPAI-B). i BPL is the bimonthly cumulative precipitation measured by the National Basic Meteorological Station (Kaihua Station) from March to November. i is the critical value of bimonthly cumulative precipitation (see Table 2 for the values). i -BP i ) is a negative value, the value is "0". The calculation results are shown in Table 1.

[0112] Table 2 Critical values ​​of bimonthly cumulative precipitation in Kaihua County during the growing season (unit: mm)

[0113] j value <![CDATA[BP i ]]> <![CDATA[BPL i ]]> j＝3 <![CDATA[BP3: Sum of precipitation in February and March]]> <![CDATA[BPL3=100]]> j＝4 <![CDATA[BP4: Sum of precipitation in March and April]]> <![CDATA[BPL4=100]]> j＝5 <![CDATA[BP5: Sum of precipitation in April and May]]> <![CDATA[BPL5=100]]> j＝6 <![CDATA[BP6: Sum of precipitation in May and June]]> <![CDATA[BPL6=200]]> j＝7 <![CDATA[BP7: Sum of precipitation in June and July]]> <![CDATA[BPL7=300]]> j＝8 <![CDATA[BP8: Sum of precipitation in July and August]]> <![CDATA[BPL8=300]]> j＝9 <![CDATA[BP9: Sum of precipitation in August and September]]> <![CDATA[BPL9=200]]> j＝10 <![CDATA[BP 10 : The sum of precipitation in September and October]]> <![CDATA[BPL 10 =100]]> j＝11 <![CDATA[BP 11 : The sum of precipitation in October and November]]> <![CDATA[BPL 11 =100]]>

[0114] (5) Calculation of annual sunshine anomaly index (YSAI). i Sh is the monthly sunshine hours measured by the national basic meteorological station (Kaihua Station) from January to December. i is the critical value of monthly sunshine hours (see Table 3 for the values). i -Si ) is negative, the value is "0". Because the climate index did not reach the 0.05 level significance test in the MLR stepwise regression equation, the calculation results are not listed.

[0115] Table 3 Critical values ​​of sunshine hours in Kaihua County for each month (unit: h)

[0116] <![CDATA[Sh1=85]]> <![CDATA[Sh2=100]]> <![CDATA[Sh3=100]]> <![CDATA[Sh4=130]]> <![CDATA[Sh5=130]]> <![CDATA[Sh6=150]]> <![CDATA[Sh7=220]]> <![CDATA[Sh8=220]]> <![CDATA[Sh9=150]]> <![CDATA[Sh 10 =150]]> <![CDATA[Sh 11 =100]]> <![CDATA[Sh 12 =85]]>

[0117] 4. Construction of the “Forest Carbon Sequestration-Climate Index” Model

[0118] The Multiple Linear Regression (MLR) statistical stepwise regression method was used to construct the "Forest Carbon Sequestration-Climate Index" (FCS-CI) model of Kaihua County with the calculated climate indices such as WTAI, STAI, GSPAI-S, GSPAI-B and YSAI as independent variables and the forest carbon sequestration (DTNPP) calculated in the S2 step as the dependent variable.

[0119] The obtained FCS-CI model equation determination coefficient R value reached 0.897, passing the 0.01 level significance test; the three climate index factors, namely GSPAI-S, GSPAI-B and WTAI, passed the 0.01 level significance test, the STAI climate index factor passed the 0.05 level significance test; the YSAI climate index factor did not pass the 0.05 level significance test; the regression parameters of each factor of the FCS-CI model are shown in Table 4.

[0120] Table 4 Regression parameters of FCS-CI model in Kaihua County

[0121]

[0122] The meaning of each regression parameter in Table 4 is as follows: B0 represents the annual carbon sequestration per square meter of Kaihua County forest under the "good weather and good harvest" climate conditions (unit: gC); A1, A2, A3, and A4 represent that for every unit increase in GPAI-B, WTAI, STAI, and GPAI-S, the carbon sequestration decreases by approximately 12.96, 13.17, 13.45, and 0.24 (unit: gC), respectively. The simulation errors of the FCS-CI model in Kaihua County are shown in Tables 5 and Figure 3 .

[0123] Table 5 Simulation error of the “forest carbon sequestration-climate index” model in Kaihua County

[0124] years 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 DTNPP 1022 1051 1098 986 1109 967 1027 1110 1070 1074 1018 1023 FCS-CI 1053 1081 1075 986 1071 965 1027 1077 1038 1066 1041 1042 error 3.0％ 2.9％ 2.1％ 0.0％ 3.4％ 0.3％ 0.0％ 3.0％ 3.0％ 0.7％ 2.2％ 1.8％ years 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 DTNPP 1043 928 1085 1041 1020 1072 1056 1007 1104 1121 966 FCS-CI 1023 947 1053 1070 991 1067 1032 1033 1060 1072 944 error 2.0％ 2.2％ 3.0％ 2.7％ 2.9％ 0.5％ 2.3％ 2.7％ 3.9％ 4.4％ 2.2％

[0125] From Table 5 and Figure 3It can be seen that the constructed FCS-CI model of Kaihua County has good simulation ability, the change trend of the FCS-CI model simulation value and the DTNPP value are basically consistent, and the absolute value of the relative error of the model simulation is only 2.2% on average, of which: the simulation error of 9 years is lower than 2.0%, and only 1 year has a simulation error higher than 4.0%. This shows that the constructed FCS-CI model has a strong quantitative assessment ability in characterizing the impact of abnormal climate conditions on forest carbon sequestration, especially in the severely affected years (2013, 2005, 2013, 2022), the model simulation ability is more prominent.

[0126] 5. Design of Climate Index Insurance Model for Forest Carbon Sinks (CIIFCS)

[0127] The forest carbon sink loss rate (or loss value) is calculated using the constructed FCS-CI model, and the historical occurrence probability of different levels of climate indexes such as WTAI, STAI, GSPAI-S, GSPAI-B and YSAI from 1971 to 2022 and the extreme value of the 100-year risk of each climate index (calculated using the Gumble extreme value model) are comprehensively considered to determine the critical conditions for triggering insurance claims and the insurance claims rule model of the CIIFCS model. Lmax-G100 is the forest carbon sink loss corresponding to the 100-year climate index value (WTAI = 15 ° C, STAI = 10 ° C, GSPAI-S = 9, GSPAI-B = 865 mm) calculated by the Gumble extreme value model.

[0128] Determination of critical conditions for triggering claims: Based on the probability of occurrence of each level of climate index (WTAI, STAI, GSPAI-S, GSPAI-B and YSAI) from 1971 to 2022, the climate index value corresponding to the probability of occurrence of 75-85% is set as the critical condition for triggering insurance claims, see Table 6.

[0129] Table 6 Critical conditions for triggering claims for forest carbon sink climate index insurance in Kaihua County

[0130]

[0131] A climate index insurance claim rule model was designed for four types of insurance: low temperature in winter (insurance type A), high temperature in summer (insurance type B), more precipitation in the growing season (insurance type C), and less precipitation in the growing season (insurance type D) for Kaihua County forest carbon sinks. See Tables 7 to 10.

[0132] Table 7 Forest carbon sink winter low temperature (insurance type A) insurance claim rules model

[0133]

[0134] Table 8 Forest carbon sink summer high temperature (insurance type B) insurance claim rule model

[0135]

[0136]

[0137] Table 9 Insurance claim rules model for the case of excessive precipitation during the growing season of forest carbon sink (insurance type C)

[0138]

[0139] Table 10 Insurance claim rules model for low precipitation during the growing season of forest carbon sink (insurance type D)

[0140]

[0141]

[0142] VI. Design of Forest Carbon Sink Climate Index Insurance Products

[0143] Set up Kaihua County Forest Carbon Sink Climate Index Insurance types and coverage, see Table 11. Low temperature in winter (insurance type A), protects forest carbon sink losses caused by low temperatures in winter (December to February of the following year), with WTAI as the measurement value, and sets insurance compensation standards; high temperature in summer (insurance type B), protects forest carbon sink losses caused by high temperatures in summer (June to August), with STAI as the measurement value, and sets insurance compensation standards; more precipitation in the growing season (insurance type C), protects forest carbon sink losses caused by more precipitation (rainy and lightless) during the peak forest growth season (March to November), with GSPAI-S as the measurement value, and sets insurance compensation standards; less precipitation in the growing season (insurance type D), protects forest carbon sink losses caused by less precipitation (drought) during the peak forest growth season (March to November), with GSPAI-B as the measurement value, and sets insurance compensation standards.

[0144] Table 11 Kaihua County Forest Carbon Sink Climate Index Insurance Products

[0145]

[0146]

[0147] * Note: The insurance amount is based on the maximum carbon sink loss value in Table 7, Table 8, Table 9 and Table 10, and is calculated based on the maximum carbon sink loss value of 100gC / m 2 The economic loss of 367 yuan / hectare is taken as a reference value and is calculated based on the full insurance standard (i.e., the insurance amount is the same as the insured carbon sink value). Figure 4 This is a trial calculation example of the implementation plan for Kaihua County from 2000 to 2022.

[0148] Table 12: Kaihua Forest Carbon Sink Climate Index Insurance Claims and Claims from 2000 to 2022

[0149]

[0150] Figure 4 It can be seen that the forest carbon sink climate index insurance model and insurance products designed by the present invention have strong practical operability. The amount of forest carbon sink loss in each year in the embodiment basically corresponds to the insurance claim amount, especially in years with particularly abnormal climatic conditions (2003, 2005, 2013 and 2022), the largest degree of forest carbon sink loss corresponds to a high amount of compensation.

[0151] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0152] The present invention provides a forest carbon sink insurance method based on climate index. By designing a climate index that can characterize the degree of abnormal climate conditions, a "Forest Carbon Sequestration-Climate Index" (FCS-CI) model is constructed, and a forest carbon sink insurance technology based on the climate index is developed. It can quickly and efficiently quantify the climate losses of forest carbon sinks caused by abnormal climate conditions only by using the observation data of national basic meteorological stations, solve the problems of human interference and model differences, and make up for the deficiency of satellite remote sensing that cannot obtain data due to weather reasons. It provides an original technology for the customization of forest carbon sink insurance types and objective and efficient disaster loss assessment.

[0153] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A forest carbon sink insurance method based on climate index, characterized by: The following steps are included: S1. Climate index design: Design the winter temperature anomaly index to quantify the degree of abnormally low winter temperature, the summer temperature anomaly index to quantify the degree of abnormally high summer temperature, the growing season precipitation anomaly index to quantify the degree of abnormally high growing season precipitation, the growing season bimonthly precipitation anomaly index to quantify the degree of abnormally low bimonthly precipitation in the growing season, and the annual sunshine anomaly index to quantify the degree of abnormally low annual sunshine. S2. Construction of forest carbon sequestration-climate index model: The forest carbon sequestration-climate index model was constructed using the multivariate linear regression statistical method, with the climate index as the independent variable and the annual forest carbon sequestration as the dependent variable. S3. Design of claim rules model for forest carbon sink climate index insurance: Based on the forest carbon sink loss rate calculated by the forest carbon sequestration-climate index model, and taking into account the historical occurrence probability of different levels of the climate index and the extreme risk values ​​of future climate change, the critical conditions for triggering insurance claims and the insurance claims rules of the forest carbon sink climate index insurance model are determined.

2. The forest carbon sink insurance method based on climate index according to claim 1, characterized in that: Step S1 specifically includes the following contents: S11. Calculate the winter climate anomaly index to quantify the degree of abnormally low winter temperatures and use it to measure the forest carbon sink loss caused by low winter temperatures; The calculation formula is, WTAI=(TL-T 12 )+(TL-T1)+(TL-η2) Among them, WTAI is the winter climate anomaly index; T 12 T1, T2 are the average temperatures measured by national basic meteorological stations in December, January and February of the following year respectively; TL is the low temperature critical value; S12. Calculate the summer temperature anomaly index to quantify the degree of abnormally high summer temperature and use it to cross-reference the forest carbon sink loss caused by the high summer temperature. The calculation formula is: STAI=(T6-T h )+(T7-T h )+(T8-T h ) Among them, STAI is the summer temperature anomaly index; T6, T7, and T8 are the monthly average temperatures measured by national basic meteorological stations in June, July, and August respectively; T h is the high temperature critical value; S13. Calculate the growing season precipitation anomaly index to quantify the degree of abnormal precipitation in the growing season and use it to measure the forest carbon sink loss caused by excessive precipitation during the peak forest growth season; the calculation formula is, Among them, GSPAI-S is the growing season precipitation anomaly index; SP i is the monthly precipitation measured by the national basic meteorological station in the i-th month, i = 3 to 11; SP h is the critical value of precipitation in a single month; S14. Calculate the bimonthly precipitation anomaly index during the growing season to quantify the degree of bimonthly precipitation anomaly during the growing season and use it to measure the forest carbon sink loss caused by less precipitation during the peak forest growth season. The calculation formula is: Among them, GSPAI-B is the bimonthly precipitation anomaly index during the growing season; BP i is the bimonthly cumulative precipitation measured by the national basic meteorological station in the i-th month, i = 3-11; BPL is the critical value of bimonthly cumulative precipitation; S15. Calculate the annual sunshine anomaly index to quantify the degree of annual sunshine anomaly and use it to measure the forest carbon sink loss caused by insufficient sunshine. The calculation formula is: Among them, S i is the monthly sunshine hours measured by the national basic meteorological station in the i-th month, i = 1~12; Sh is the critical value of the monthly sunshine hours.

3. The forest carbon sink insurance method based on climate index according to claim 2, characterized in that: In step S2, the formula for constructing the forest carbon sequestration-climate index model is: NPP=B0-A1*(WTAI)-A2*(STAI)-A3*(GPAI-S)-A4*(GPAI-B)-A5*(YSAI) Among them, NPP is the annual carbon sequestration of the forest; B0 is the regression constant term of the MLR equation; A1, A2, A3, A4, and A5 are the regression coefficients of the MLR equation corresponding to the WTAI, STAI, GPAI-S, GPAI-B, and YSAI climate index terms, respectively.

4. The forest carbon sink insurance method based on climate index according to claim 3 is characterized in that: MLR uses a stepwise regression method to test the introduction of climate indices WTAI, STAI, GPAI-S, GPAI-B and YSAI at a significance level of 0.05 to determine the final form of the regression equation.

5. The forest carbon sink insurance method based on climate index according to claim 4 is characterized in that: In step S3, the calculation formula of forest carbon sink loss rate is: LR k =(L k / L max-G100 )*100 Among them, LR k is the forest carbon sink loss rate at the kth level of the climate index; L k is the forest carbon sink loss of the kth level; L max-G100 This is the forest carbon sink loss corresponding to the once-in-a-century climate index value calculated by the Gumble extreme value model.

6. The forest carbon sink insurance method based on climate index according to claim 5, characterized in that: In step S3, the critical conditions for triggering insurance claims are determined by setting the climate index value corresponding to the probability of occurrence of 75-85% as the critical conditions for triggering insurance claims based on the probability of occurrence of each level of the climate index in the past 50 years or more.

7. The forest carbon sink insurance method based on climate index according to any one of claims 1 to 6, characterized in that: Before step S1, the method further includes: S01, calculation of forest carbon sequestration per unit area: The forest net primary productivity under map projection is calculated based on digital elevation to obtain the spatial forest carbon sequestration per unit surface area of ​​the actual surface; the calculation formula is: Among them, S1 is the carbon sequestration amount in the spatial range; S n is the unit surface area of ​​the nth spatial grid unit, n=1~N, N is the total number of spatial grid units; C is the forest carbon sequestration per unit area.

8. The forest carbon sink insurance method based on climate index according to claim 7, characterized in that: Between S01 and S1, S02, biological correction of forest carbon sequestration time series: Based on the forest carbon sequestration per unit area in space, the biological trend of the natural growth of forest carbon sequestration capacity due to tree age increase implied in the forest carbon sequestration time series is eliminated to obtain the biologically corrected forest carbon sequestration time series; The calculation formula is, DTNPP j =NPP j +(T m -T j )×b Among them, NPP j and DTNPP j are the forest carbon sequestration in the jth year before and after biological correction, respectively; T m is the year corresponding to the maximum annual value of forest carbon sequestration in the annual time series; T j is the jth year, j = 1~J, J is the total number of years; b is the linear trend rate of forest carbon sequestration; T f The last year in the annual time series; NPP MEAN is the mean NPP of all years in the annual time series.

9. The forest carbon sink insurance method based on climate index according to claim 1, characterized in that: After step S3, the following steps are also included: S4, forest carbon sink climate index insurance product design: Set up insurance types and protection contents, specifically including five product types: Type A for low temperature in winter, Type B for high temperature in summer, Type C for more precipitation during the growing season, Type D for less precipitation during the growing season, and Type E for fewer annual sunshine hours.

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