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.

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.

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.

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.

Estimating the properties of weathered bedrock and pile-rock interaction from the geological strength index

The effect of variable rock mass properties on pile-rock interaction poses a great challenge to the design of stabilizing piles and numerical analysis of pile-rock interaction. The paper presents a novel method to estimate the properties of weathered bedrock, which can be applied to routine design of landslide-stabilizing piles for collivial landslides. The Ercengyan landslide located in the Three Gorges Reservoir, China, is the area of interest for this study. A geological investigation and triaxial tests were conducted to estimate the basic parameters, including Geological Strength Index(GSI), uniaxial compressive strength σ_(ci) and Hoek-Brown constant m_i of intact bedrock in the study area. Hoek-Brown criterion was used to estimate mechanical properties of the weathered rock, including elastic modulus E_m, cohesion c, friction angle Φ, and normal ultimate lateral resistance p_(max). A parametric study was performed to evaluate the effect of parameterizations of GSI, σ_(ci) and m_i on the bedrock properties and p-y curves. The estimated rock mass properties were used with PLAXIS 2D software to simulate pile-rock interaction. Effect of GSI on stress at the pile-rock interface and in the rock, pile bending moment, pile shear force, and p-y curve were analysed.

2024年初两次雨雪冰冻天气过程对公路通行影响的对比分析

2024年1月31日-2月5日、2月18-25日我国先后出现两次大范围雨雪冰冻天气过程(分别简称0131"过程和"0218"过程),两次过程具有相态复杂、雨雪量大、影响范围广、持续时间长等特点。利用全国2430个国家气象站观测数据以及交通流量和公路阻断信息,对比分析了两次雨雪冰冻天气过程对公路通行的影响;同时,选取降雪量、冻雨、积雪深度、地面温度、地面结冰作为影响公路通行的气象指标,构建冰雪天气指数,探讨冰雪天气强度与公路通行影响的关系。结果表明:两次过程发生于春节前后人口流动高峰期,"0218"过程影响范围更广,造成交通流量的降低幅度总体高于"0131"过程;两次过程中陕西、山西、河南、湖北、湖南以及贵州6省公路通行受积雪或结冰影响严重,路段封闭段次多,但影响程度存在地区差异,其中山西、河南、湖北、湖南"0131"过程影响更重,陕西、贵州"0218"过程影响更重;两次过程中冰雪天气指数强度与公路阻断影响度的变化趋势一致,与交通流量变化率呈反比,反映出冰雪天气指数对于冰雪天气下的公路交通影响研判具有良好指示意义。

基于新遥感生态指数环境质量动态监测——以阜新市为例

气候灾害易影响生态环境,传统的遥感生态指数(RSEI)缺乏气候因素影响生态质量的研究,本研究引入气象因子并优化了生态因子的选择,更加综合全面评价区域内的生态环境质量。使用主成分分析法构建新遥感生态指数(NRSEI),对各因子及多因子交互分析。结果表明,NRSEI较RSEI各因子在第1主成分贡献率均值提升12.53%,并且NRSEI与各因子相关性为高度相关,3 a均值为0.853。分析了2013-2020年阜新市生态环境的变化特点和原因,并利用NRSEI对研究区域的生态环境质量进行动态监测和评估,总体来看,2013-2020年阜新市生态环境状况变好,对于阜新市与NRSEI相关性最高的单个因子是湿度因子,因此阜新市相关部门应注重对河流、湖泊和湿地的保护,减少建筑开发,以改善城市的环境质量。

中国主要城市气候舒适度时空特征

人居气候舒适性是评判城市环境宜居水平的重要因素之一。利用1991-2020年中国31个主要城市的每日气象要素资料,采用室外天气舒适指数(OWCI)模型,分析了中国主要城市人居气候舒适性总体水平、舒适度年内变化类型、冬夏两季的空间差异,并剖析了城市的夏季炎热特征及其变化趋势。结果表明,中国大多数城市年平均气候舒适度高于舒适等级水平,但空间差异明显,31个主要城市一年中舒适天气累计日数均超过半年,年平均舒适日数为231天,总体人居气候环境良好。中国31个主要城市气候舒适性年内变化可分为寒冬凉夏型、冷冬凉夏型、冷冬热夏型、暖冬长夏型及四季舒适型5种类型;春秋季节天气普遍舒适,冬夏两季气候舒适性空间差异很大,冬季的差异大于夏季的,12座城市适合夏季避暑,比较适合冬季避寒的城市有海口、广州、昆明、南宁和福州。夏季酷热的城市主要分布于长江中下游流域,近30年其夏季的酷热日数和酷热程度有较明显的增加趋势。

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。

恶劣天气下机场离场航班运行韧性评估及恢复

为保障恶劣天气下机场整体性能,科学评估机场离场航班运行韧性,提高航班恢复能力,从而有效缓解天气的影响。给出机场离场航班运行定义,从机场离场航班运行系统性能出发,分析航班离场延误时间、离场总延误时间、离场航班正常率和机场离场航班运行系统综合韧性指数4个指标,对系统在恶劣天气条件下的韧性变化进行评估;提出机场离场航班运行系统性能恢复策略,利用遗传算法对离场延误航班进行优化排序;以2012年北京首都国际机场“721”特大暴雨事件为实例进行数据分析,得到暴雨影响下首都机场的性能指标和韧性指数,对比分析机场离场航班运行系统性能及韧性水平变化。研究结果表明:受暴雨影响,机场离场航班运行系统综合韧性指数由0.457 3下降到0.062 8,暴雨减小后上升到0.222 3;在进行航班优化排序后,离场航班总延误时间减少了24.85%,优化后机场性能恢复速度提升了13.89%,优化后的机场离场航班运行系统最小韧性指数提升了13.38%,系统性能优先恢复至初始状态,表明所提恢复策略有效。

我国本土气温指数期货产品的研发设计——源于京津冀“首都经济圈”核心功能区的气温数据

我国幅员辽阔,对天气变化非常敏感,迫切需要对天气风险进行有效管理,以消除或减少天气风险对各行业的不利影响。从产品研发设计视角出发,以京津冀“首都经济圈”核心功能区为例,参考国外成熟产品,对我国本土气温指数期货产品进行合约设计和模拟定价,根据行业需求阐述产品的应用价值,提出产品在天气风险管理中的显著作用。研究贡献有,搭建起我国本土气温指数期货产品的研发体系;基于国外成熟市场规则构建出符合我国国情的气温指数期货产品标准化合约框架;运用ARMA模型构建出精确度高的气温预测模型;采用蒙特卡罗模拟法制定出气温指数期货两款产品价格;明确产品的应用推广需求和风险管理作用。

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。

四川盆地须家河组诺利—瑞替期之交气候变迁及其驱动机制

【目的】晚三叠世诺利—瑞替期之交(Norian-Rhaetian Transition,NRT)的古气候—环境发生了显著的变化,且伴随着全球性的碳同位素负漂和生物绝灭事件。目前多数NRT研究均聚焦于特提斯域的浅海相地层,而关于NRT时期陆地的气候与环境变迁及其驱动机制的研究还非常缺乏。【方法】为了解决这一科学问题,对四川盆地西北部以河流相和沼泽相沉积为主的诺利—瑞替阶剖面(须家河剖面)进行研究。利用主、微量元素分析,重建了四川盆地须家河组NRT时期的古气候演化过程并探讨了其驱动机制。【结果】须家河组诺利末期(剖面:105.5~129.5 m)古气候指数CIA、Rb/Sr相对较高,R值相对较低(平均值分别为73、1.2、8.2);诺利—瑞替期界线处(剖面:129.5~135 m)CIA、Rb/Sr相对较低,R值相对较高(平均值分别为61、0.5、13.5)。【结论】四川盆地须家河组NRT时期气候波动频繁,诺利末期以温暖湿润的气候为主,到了诺利—瑞替期界线附近,发生了一次短暂的降温事件。须家河组NRT时期湿暖—干冷的古气候变化和诺利—瑞替期界线处的降温事件主要受到晚三叠世盛行于泛大陆中的超级季风控制,但不排除同时期的火山活动和野火事件对古气候系统的影响,然而火山活动、野火事件以及气候环境变化三者间的互馈机制,需要进一步明晰。

琼中地区中三叠世黑云母二长花岗岩成壤过程的量化指标表征

土壤是地球关键带的重要组成部分,是岩石风化的结果,是连接岩石圈、生物圈、大气圈和水圈的纽带,其形成、发育和功能将深刻影响地球生态系统的可持续性(朱永官等,2015)。由岩石到土壤的风化过程中,土壤过程是控制地球关键带中物质、能量、信息流动与转化的重要节点。受成壤母质、气候等因素的影响,元素的活动性表现出不同的特点规律。不同学科通过不同量化指标表征成壤过程中元素的规律。

基于TLE数据的大气密度反演与校正

用于航天器轨道预报的热层密度模型普遍存在30%左右的误差,影响LEO卫星的精密轨道确定和载荷控制。基于低轨航天器平运动变化与大气密度的关系,使用GRACE(gravity recovery and climate experiment)卫星TLE数据反演2003、2007年沿轨大气密度,通过比较反演值、模型值和实测值的关系分析误差产生原因,使用对数正态分布拟合密度比值。通过分析太阳辐射、地磁指数对大气密度变化的影响,提出一种基于空间环境指数的热层大气密度模型校正与预报方式。使用该方法对2003、2004、2007、2008年的MSIS86模型计算密度进行修正,将模型平均相对误差从33.33%~59.62%降低到11.55%~15.13%,太阳活动低年改进量是高年的1.5~2倍。对2009年经验模型结果进行预报校正,将预报误差降低36.49%,提高了模型精度。

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)。

安徽省强对流天气灾害的时空分布特征研究

文章基于1984—2009年安徽省75个县(市)强对流天气灾害的出现次数和灾情情况等资料,以受灾人数、死亡人数、受伤人数、倒塌房屋、损伤房屋、农作物受灾面积、电力倒杆、直接经济损伤、农业经济损失等9个灾情要素,通过线性组合法构建了强对流天气灾害的综合灾损指数,并依据灾损指数确定了强对流灾害的危害程度,分为一般、较重、严重、特重4个等级。结果表明:1984—2009年安徽地区的强对流天气灾害的年出现次数和年灾损指数总体呈线性上升趋势,较重、严重、特重等级灾害表现出增长趋势;安徽省强对流天气灾害频发的月份是6—7月,夏季是灾害最频发的季节;皖北地区的灵璧县、砀山县、萧县是强对流天气灾害最严重的地区。该研究可为安徽省的强对流天气防灾减灾工作提供参考。

近10年深圳市短时强降水及风险统计分析

利用深圳市近10年全市自动气象站逐时降水量资料,分析深圳市短时强降水气候特征、风险指数、影响系统,结果表明:(1)深圳市短时强降水日数呈现逐年增长的趋势,2009年至今一直处于高位波动中。短时强降水日数月变化呈现倒U型分布,6—8月是防范短时强降水风险的关键时期,中午至傍晚是短时强降水的易发时段。(2)建立了短时强降水风险指数,结果表明该指数可以指示各区的高风险地区。(3)深圳市短时强降水的天气系统分为5种类型:热带气旋和热带扰动外围环流影响、切变线和冷空气影响、季风低压、西南风急流辐合、副高边缘东风波动。