《保险研究》20210201-《全国主粮作物减产风险评估与保险费率厘定研究》（叶涛、陈说、刘苇航、牟青洋、史培军、张兴明）

［摘   要］《关于加快农业保险高质量发展的指导意见》对开展农业保险风险区划提出了具体要求。本研究针对水稻、小麦、玉米三大主粮作物,利用作物模型集合模拟器、配合县级历史单产统计数据,使用单产时空融合方法还原了1991~2016年全国10 km栅格水平的单产损失率,厘定了保险纯风险损失率和大灾风险附加费率。结果显示,在100%保障水平下,10 km栅格尺度上的水稻纯风险损失率的均值为3.8%、标准差为3.1%；小麦纯风险损失率的均值为5.9%、标准差为3.7%；玉米纯风险损失率的均值为5.8%、标准差为3.4%。全国范围内统一分散大灾风险的前提下,20年一遇大灾风险附加因子分别为0.85(水稻)、0.64(小麦)和0.7(玉米)。本文的评估方法有效解决了时间序列插补和空间降尺度的问题,可提供较高空间分辨率的评估结果、支撑各级行政单元的费率厘定,且具有较强的原生性、可配合实际需求计算不同保障水平的费率。本文的研究结果可为新一轮农业保险区划工作提供重要量化依据和技术支撑。

［关键词］减产风险评估；纯风险损失率；大灾风险附加费率；费率厘定

［基金项目］国家重点研发计划“全球变化人口与经济系统风险全球评估(2016YFA0602404)”资助。

［作者简介］叶涛,北京师范大学地理科学学部灾害风险科学研究院、应急管理部-教育部减灾与应急管理研究院教授、博士,研究方向:农业风险与灾害保险；陈　说、刘苇航、牟青洋,北京师范大学地理科学学部灾害风险科学研究院硕士研究生,研究方向:全球变化与灾害风险；史培军(通讯作者),青海师范大学高原科学与可持续发展研究院教授,北京师范大学地理科学学部灾害风险科学研究院、应急管理部-教育部减灾与应急管理研究院教授、博士,研究方向:环境演变与自然灾害；张兴明,中国银行保险信息

Risk Assessment of Yield Loss and Insurance Premium Rating for Staple Crops in China

YE Tao,CHEN Shuo,LIU Wei-hang,MU Qing-yang,SHI Pei-jun,ZHANG Xing-ming

Abstract：The Guiding Opinions on Accelerating the High-quality Development of Agricultural Insurance put forward specific requirements on the agricultural insurance risk regionalizing.Focusing on three major staple crops in China,namely rice,wheat and maize,this study reconstructed historical yield records (1991-2016) at finer scales (10km) by spatial-temporal fusion of county-level statistical yield and simulation yield derived from crop model emulator ensembles.From the yield dataset,this study computed the pure risk loss ratio and the loading for catastrophes.Our results shows that at the 100% coverage level,the average pure risk loss ratios at the 10-km grids are 3.8% (rice),5.9% (wheat) and 5.8% (maize),and the standard deviations are 3.1% (rice),3.7% (wheat) and 3.4% (maize),respectively.If setting up insurance pools at the national level,the catastrophic loading factors are 0.85 (rice),0.64 (wheat) and 0.7 (maize),respectively,for catastrophes rare in 20 years.The method developed in this paper effectively solves the problems of time series interpolation and spatial downscaling,and can provide the results with the finer spatial resolution to support the rate making at different administrative level.This method has strong flexibility that can be used to calculate the rates of any coverage level according to practical requirements.The results of this paper can provide an important quantitative basis and technical support for the new round of agricultural insurance risk regionalization.

Key words：assessment of yield loss risk;pure risk loss ratio;loading for catastrophes;rate making