Weather index insurance for wind energy

As China vigorously promotes the development of new energy,photovoltaic power curtailment and wind power curtailment have been effectively resolved.At the same time,the yield from new energy power generation is becoming an important factor that affects the scale of investment in new energy.This paper focuses on the weather risks faced by wind power producers.By studying current research on weather index insurance in China and abroad,the functions and design methods for weather index insurance have been clarified.In addition,the feasibility of wind-power generation index insurance is discussed.The calculation methods for wind power generation index and the weather index insurance pricing methods for wind power enterprises are proposed.A weather index insurance model for wind power generation was established.The rationality and feasibility of the weather index insurance model proposed in this paper were verified using data from an existing power plant.The simulation results show that wind power enterprises can effectively avoid economic losses caused by weather risks through weather index insurance.

Financial Innovation Way of Avoiding Risk of Climate Disasters in Food Production:Weather Index Insurance

Climate change will lead to a variety of climate disasters, and climate disasters have a greater impact on China＇s food production. Weather index insurance is a new financial way to avoid risk of climate disasters effectively in China＇s food production. Firstly, the relationship between weather index insurance and food production in China was elaborated, and then the development status, advantages and disadvantages of weather index insurance in China at present were analyzed. Finally, some countermeasures against the problems of weather index insurance in China were put forward.

Scheme of Weather-Based Indemnity Indices for Insuring Against Freeze Damage to Citrus Orchards in Zhejiang,China

We design a weather-based indemnity index for the insurance against freeze damage to citrus orchards so as to provide technological support for the development of policy-based agriculture. The indices are prepared by separating a relative meteorological yield from the yield that is dependent on tree age, high-yield and low-yield years, and environmental factors, and then using a risk assessment scheme to determine the percentage yield reduction due to the meteorological hazard. We thus develop a set of indices associated with cold temperature damage with which to construct more severe weather indices in conjunction with the yield percentage decrease. We then combine the insured regional citrus yield index with the insured meteorological counterpart to obtain a weather-based indemnity index for the varying degree of freeze damage to crops. When the freeze damage index （FDI） is greater than -7.0℃ for the coastal belt of Zhejiang Province, China, or greater than -9.0℃ for other regions of Zhejiang, weather-based indemnity index （WBII） is zero, meaning there is no compensation; when the FDI is from -7.0 to -7.9℃ for the coastal belt or from -9.0 to -9.9℃ for other regions, the WBII is 1 with 50% compensation; when the FDI is from -8.0 to -8.9℃ for the coastal belt or from -10.0 to -10.9℃ for other regions, the WBII is 2 with 70% compensation; and when the FDI is less than -9.0℃ for the coastal belt or less than -11.0℃ for other regions, the WBII is 3 with 90% compensation. The weather indemnity indices of insured orchards are developed in the interest of owners, thereby eliminating adverse selection and moral hazard issues and providing timely recompense from the insurer, and resolving the problem of high indemnity cost in agricultural insurance.

印度农作物天气指数保险计划的制度安排与政策启示

农作物天气指数保险因其在赔付效率、交易成本等方面的优势,被认为是传统农业保险产品的有益补充甚至是替代。在实施天气指数保险的国家当中,印度是最具有代表性的国家之一。本文在简要梳理印度农作物天气指数保险计划(RWBCIS)在其农业保险体系构建中的定位及其演进历程的基础上,系统阐述其产品设计、运营体系以及监督约束机制等具体措施和实施经验。研究发现,印度将RWBCIS定位为传统农作物产量险的有效补充,并结合地理位置、耕地分布及气候类型下的适用性,发挥二者的协同效应;RWBCIS的有序实施得益于完备的保险基础数据、健全透明的信息平台、精细化的产品设计和覆盖面广泛的气象网络的共同支撑;通过制定投保作物日历以及监督惩罚机制明确权责分配,提高保险市场的稳定性及可持续性。印度实施RWBCIS的政策思路及其在保险运行机制设计上的具体安排,或为中国农业保险顶层制度设计以及产品创新提供值得借鉴的启示。

福建漳州平和柚天气指数保险产品设计与定价

柚产业是福建省漳州市平和县的支柱产业,为分散当地柚生产的经营风险,基于福建省漳州市平和县1985−2022年柚产量及对应年份3月中旬至8月上旬的天气数据,将柚单产数据分解成趋势单产和气象单产,并调整单产数据使其转化到2024年的生产力水平下的单产,选用核密度估计法拟合该序列的概率密度函数并用于纯费率厘定;而后利用调整后单产数据和天气数据进行相关性分析,筛选显著的天气指数。结果显示:4月中下旬降雨量指数与调整后单产的线性关系最为显著。对于2024年约定的保障产量51000 kg·hm^(−2)和保险价格2.8元·kg^(−1),即每667m^(2)保险金额为9520元,厘定纯费率为4.0191%、保险费率为4.6220%;对于降雨量指数RI的保险赔付触发值160 mm以及RI的8个分级赔付区间,分别计算得到了对应的赔付事件发生概率,并给出了对应的赔付金额计算公式。

安徽主要粮食作物天气指数保险模型研究与应用

气象灾害是农业生产面临的主要风险之一,给农业安全生产带来巨大损失。农业保险是农业保障体系的重要组成部分,是有效分散农业风险或弥补灾害损失的重要救灾手段之一。20世纪90年代后期出现的天气指数保险,因其客观、标准化和流通性强等特点在全球引起广泛关注、研究与应用。本文在回顾了国内外天气指数保险技术的发展历程基础上,详细分析了天气指数保险与传统农业保险的差异,以及各自的优势与劣势;以安徽粮食作物(冬小麦、一季中稻、夏玉米)生长过程中特定农业气象灾害的天气指数保险为例,介绍天气指数模型研制与产品开发。通过天气指数农业保险产品在安徽多年实践应用,探索天气指数模型的改进方法,以降低基差风险;并对天气指数保险未来发展进行探讨与展望。

黄河口大闸蟹高温天气指数保险产品设计与研发

利用1981-2021年东营国家气象观测站逐日气温观测数据、2019-2021年黄河口农业水域小气候观测站逐日气温和水温观测数据以及2004-2021年东营市黄河口大闸蟹产量数据,采用滑动平均法分离气象产量后计算气象减产率,分析大闸蟹生长周期内温度和减产率的关系,筛选极显著相关的高温天气指标,构建高温指数-灾损模型,确定灾害损失阈值,设计研发黄河口大闸蟹高温天气指数保险产品。结果表明:随着生产技术的提高,黄河口大闸蟹增产明显;受不同年份气象条件差异影响,黄河口大闸蟹气象减产率波动明显,减产率与≥35℃有效积温日均值呈极显著正相关(P<0.01)。结合1981-2021年高温气象资料,确定≥35℃的有效积温日均值即高温指数达1.893℃·d,可导致大闸蟹减产12.4%;依高温指数-灾损模型可进一步将黄河口大闸蟹高温灾害分为轻度、较轻、中度、较重和重度五级,并取高温指数≥1.529℃·d作为理赔触发值计算可发生赔付年份的平均赔付金额为294.04元667m^(−2),赔付金额范围为58.59~651.00元667m^(−2);黄河口大闸蟹高温天气指数纯保险费率为6.61%,保费为43.03元667m^(−2)。

多层次农业保险目标下理论与实践协同的挑战——以天气指数保险创新为例

发展多层次农业保险须在理论可行与实践检验的基础上开展保险产品创新。以天气指数保险为例,尽管传统观点认为其具备破解信息不对称引发的逆向选择问题这一理论优势,可满足地方特色经济作物多样化风险管理的需求。但受限于试点范围有限、时间短,现有论证缺乏有效现实依据支撑。本文将理论与现实情景相结合,采用单案例纵向分析的方式重点针对“天气指数保险可规避逆向选择”的主流理论观点进行实践检验,补充了观点成立的重要前提条件。研究发现:受限于未能根本性消除个体特征风险,天气指数保险本身仍面临逆向选择问题。同时,气象指标测量精度与产品设计精细化程度相匹配是天气指数保险缓解逆向选择问题的重要前提条件之一。二者不匹配会进一步强化个体特征风险差异、放大不同农户个体特征风险与区域平均风险水平的关联差异,加剧逆向选择问题,进而引发天气指数保险不可持续危机。通过推进农业保险产品创新理论与实践效果的协同优化,本研究为发展多层次农业保险提供重要启示。

Farmers' Risk Preferences and Agricultural Weather Index Insurance Uptake in Rural China

The purpose of this study is to investigate the effect of farmers' risk preferences on their decisions to purchase the agricultural weather index insurance based on the evidence from a household survey and field experiments conducted in Yongqiao District,Suzhou City of Anhui Province in China.Our empirical results show that farmers' risk aversion significantly increases the probability of their decision to buy weather index-based crop insurance.Other factors that significantly influence weather index insurance participation decisions include farmers' subjective beliefs on the probability of crop losses,farming experience,education level,farm size,and their household income.The empirical results of this study can provide helpful insights for policymakers and local insurers to further improve farmers' participation in weather indexbased crop insurance.

Performance of Temperature-Related Weather Index for Agricultural Insurance of Three Main Crops in China

In this study, two categories of weather index—absolute index and relative index—for chilling injury and heat damage of three main crops in China were assessed to identify insurable counties. First, correlations between selected weather indices and yield losses were examined for each county. If a correlation was significant, the county was categorized as ‘‘insurable'' for the corresponding hazard or index. Second, the spatial distribution of insurable counties was characterized and finally, their correlation coefficients were analyzed at various spatial scales.The results show that the spatial patterns of insurable areas varied by categories of weather indices, crops, and hazards.Moreover, the weather indices based on relative threshold of temperature were more suitable for chilling injury in most regions, whereas the indices based on absolute threshold were more suitable for heat damage. The findings could help the Chinese government and insurance companies to design effective insurance products.

Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data-a machine learning approach

Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historical records,global geographical coverage and fine spatial resolution at the same time,which is nearly impossible to satisfy,especially with open-access data.In this paper,we spatially downscaled gridded climate data(precipitation,temperature,and soil moisture)in coarse spatial resolution with globally available longterm historical records to finer spatial resolution,using satellite-based data and machine learning algorithms.We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield.This study employed countylevel spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia.The results showed that in the majority of cases(70%),hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach.These improvements are statistically significant(p≤0.05).Among other climate data,more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data.Overall,this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.

Farmers’Demand for Informal Risk Management Strategy and Weather Index Insurance:Evidence from China

In the absence of formal risk management strategies,agricultural production in China is highly vulnerable to climate change.In this study,field experiments were conducted with 344 households in Heilongjiang(Northeast China)and Jiangsu(East China)Provinces.Probit and logistic models and independent sample T-test were used to explore farmers’demand for weather index insurance,in contrast to informal risk management strategies,and the main factors that affect demand.The results show that the farmers prefer weather index insurance to informal risk management strategies,and farmers’characteristics have significant impacts on their adoption of risk management strategies.The variables non-agricultural labor ratio,farmers’risk perception,education,and agricultural insurance purchase experience significantly affect farmers’weather index insurance demand.The regression results show that the farmers’weather index insurance demand and the influencing factors in the two provinces are different.Farmers in Heilongjiang Province have a higher participation rate than those in Jiangsu Province.The government should conduct more weather index insurance pilot programs to help farmers understand the mechanism,and insurance companies should provide more types of weather index insurance to meet farmers’diversified needs.

极端天气下某防波堤工程索赔及保险赔付实践

南亚某离岸防波堤,按规范和合同要求施工,由于遭遇到极端天气,导致部分防波堤被冲毁,项目管理团队依据FIDIC条款,对极端天气进行界定,并开展防波堤施工期工程索赔和保险理赔,最大限度的减少极端天气对防波堤实施的影响,上述工程索赔及保险赔付实践可以为后续同类项目在遇到类似工况下施工期索赔及保险赔付提供借鉴。

设施越冬番茄寒冻害天气指数保险设计研究

随着气候变化加剧,寒冻害已成为影响福州地区设施越冬番茄生产的主要气象灾害之一。以设施蔬菜番茄为例,设计了一种基于天气指数的保险产品,以转移农业生产中的寒冻害风险,降低农民的经济损失。

基于CERES-Maize模型的玉米水分关键期干旱指数天气保险:以陕西长武为例

水分关键期干旱是影响玉米生长和产量的主要限制因子,构建此时期玉米干旱损失模型,研究干旱指数天气保险,对于合理设计天气指数保险和解决目前传统农业保险的困境,转移农业气象灾害风险具有重要意义。针对作物特定阶段单因子气象灾害影响难以剥离的问题,本研究在西北农林科技大学旱作农业长武试验站进行了连续3a的雨养玉米观测试验,利用田间试验数据(玉米生长发育数据、气象数据、土壤数据和田间管理数据)对CERES-Maize模型进行参数校正和验证,模拟玉米水分关键期(6月21日-8月31日)干旱对生长和产量的影响,构建干旱损失模型;依据长武1990-2019年的气象数据,利用EasyFit软件筛选出玉米水分关键期干旱指数最优分布模型,模拟干旱发生概率;结合干旱损失模型,利用纯费率精算方法厘定玉米水分关键期干旱指数保险费率;采用投影寻踪的统计方法,设计干旱指数保险赔付方案。结果表明,CERES-Maize模型校正和验证的平均绝对相对误差ARE和相对均方根误差RRMSE都小于10%,符合作物模型模拟精度的要求;模拟的干旱指数(DI)与玉米减产率(y,%)间呈显著的线性函数关系,即y=-0.55DI+107.17;Log-logistic模型对干旱指数分布的拟合精度最高,Anderson-Darling(AD)检验值仅为0.20,轻旱、中旱、重旱和特旱发生的概率分别为9.75%、5.90%、3.71%和3.50%。基于Log-logistic模型厘定的玉米水分关键期干旱指数保险费率为5.6%。在玉米生长水分关键期,干旱指数保险的起赔点为DI=185,DI≤185时,进行分级赔付。

台风灾害气象指数保险相关技术方法初探

气象指数灾害保险是目前全球范围内广泛研究的一种风险转移工具,它可以克服传统的自然灾害保险的局限性,在农业保险领域应用前景广阔。以福建省连江县为例,依据连江县台风灾害及台风活动特征,将能够对连江县造成一定损失的台风分成分别以大风、大雨和大风雨为主导的3类。结合连江站相关气象数据及概率分布建立了广义的台风灾害气象指数,最后构建了保险赔付路线图,以供有关部门参考。

江西早稻高温逼熟气象灾害指数保险费率的厘定

以江西早稻"五优157"为试材,于2012年在早稻灌浆期利用人工气候室模拟高温逼熟的气候模式(白天35℃/夜间28℃分别持续2、4、6、8、10、12、14、16d),建立早稻减产率与高温持续天数间的关系模型。再依据江西省11个市1960-2007年气象数据,采用Weibull分布模型模拟不同地区发生高温逼熟的概率,结合减产率模型确定免赔额为15%、30%、45%的保险纯费率。结果表明:"五优157"的减产率(y,%)与灌浆期高温持续天数(x)呈对数函数关系,即y=32.082lnx-22.681;鹰潭和赣州地区的高温逼熟发生概率较高,达87.5%,抚州、景德镇、宜春和萍乡等地区较低,均在70%以下;江西省高温逼熟气象灾害指数保险纯费率呈北高南低的趋势,鹰潭地区的纯费率最高,达4.707%,宜春地区的纯费率最低,为2.138%。研究认为在免赔额为30%时的纯费率较适宜,研究结果可为江西省早稻开展政策性气象指数保险提供科学依据。

温室黄瓜气象灾害保险决策支持系统设计

气象灾害保险对于降低气象灾害对设施农业生产的影响,提高农民抗风险能力有重要意义。基于气象灾害风险评估与气象指数保险理论,设计了气象灾害指数,采用农业气象产量法,定量了分析气象灾害知识与温室黄瓜产量灾损之间的关系,建立了气象灾害评价标准库。在这些研究成果的基础上,设计了基于WebGIS的温室黄瓜气象灾害保险决策支持系统。该系统能够为温室黄瓜生产提供气象灾害监测预警和保险服务,对于降低气象灾害、增强农业防灾减灾能力有重要意义。

柑橘气象指数保险合同费率厘定分析及设计

目的设计柑橘气象指数保险合同,为开展柑橘政策性农业保险提供技术支撑。方法根据柑橘减产率与气象因子的关系,应用极值理论分析导致巨灾结果的气象风险的尾部分布,确定保险费率,综合区域产量指数保险和气象指数保险优点,设计柑橘气象指数保险合同。结果确定了浙江省西部地区各县的纯保险费率和气象灾害赔付金额。淳安、兰溪、金华市区、丽水市区气象灾害纯保险费率为1.8%—3.0%,建德、常山、江山、龙游、衢州市区气象灾害纯保险费率为5.5%—6.6%。结论气象指数保险合同针对政策性农业保险经营需求设计,可以有效解决传统农业保险和国外指数保险存在的问题,设计成气象衍生品进入金融市场。