Rainfall-relatedhazards—deficitrainand excessive rain—inevitably stress crop production,and weather index insurance is one possible financial tool to mitigate such agro-metrological losses.In this study,we investiga...Rainfall-relatedhazards—deficitrainand excessive rain—inevitably stress crop production,and weather index insurance is one possible financial tool to mitigate such agro-metrological losses.In this study,we investigated where two rainfall-related weather indices—anomaly-based index(AI)and humidity-based index(HI)—could be best used for three main crops(rice,wheat,and maize)in China’s main agricultural zones.A county is defined as an“insurable county”if the correlation between a weather index and yield loss was significant.Among maize-cropping counties,both weather indices identified more insurable counties for deficit rain than for excessive rain(AI:172 vs 63;HI:182 vs 68);moreover,AI identified lower basis risk for deficit rain in most agricultural zones while HI for excessive rain.For rice,the number of AIinsurable counties was higher than the number of HI-insurable counties for deficit rain(274 vs 164),but lower for excessive rain(199 vs 272);basis risks calculated by two weather indices showed obvious difference only in Zone I.Finally,more wheat-insurable counties(AI:196 vs 71;HI:73 vs 59)and smaller basis risk indicate that both weather indices performed better for excessive rain in wheatplanting counties.In addition,most insurable counties showed independent yield loss,but did not necessarily result in effective risk pooling.This study is a primary evaluation of rainfall-related weather indices for the three main crops in China,which will be significantly helpful to the agricultural insurance market and governments’policy making.展开更多
Capacity planning is a very important global challenge in the face of Covid-19 pandemic.In order to hedge against the fluctuations in the random demand and to take advantage of risk pooling effect,one needs to have a ...Capacity planning is a very important global challenge in the face of Covid-19 pandemic.In order to hedge against the fluctuations in the random demand and to take advantage of risk pooling effect,one needs to have a good understanding of the variabilities in the demand of resources.However,Covid-19 predictive models that are widely used in capacity planning typically often predict the mean values of the demands(often through the predictions of the mean values of the confirmed cases and deaths)in both the temporal and spatial dimensions.They seldom provide trustworthy prediction or estimation of demand variabilities,and therefore,are insufficient for proper capacity planning.Motivated by the literature on variability scaling in the areas of physics and biology,we discovered that in the Covid-19 pandemic,both the confirmed cases and deaths exhibit a common variability scaling law between the average of the demand μ and its standard deviationσ,that is,σ ∝ μ^(β),where the scaling parameterμis typically in the range of 0.65 to 1,and the scaling law exists in both the temporal and spatial dimensions.Based on the mechanism of contagious diseases,we further build a stylized network model to explain the variability scaling phenomena.We finally provide simple models that may be used for capacity planning in both temporal and spatial dimensions,with only the predicted mean demand values from typical Covid-19 predictive models and the standard deviations of the demands derived from the variability scaling law.展开更多
基金supported by the National Natural Science Foundation of China(Project Number 41977405,31761143006)the State Key Laboratory of Earth Surface Processes and Resource Ecology+1 种基金the National Scholarship Fund of China Scholarship Councilsupport of Dr.Daniel Osgood of the International Research Institute for Climate and Society,Columbia University。
文摘Rainfall-relatedhazards—deficitrainand excessive rain—inevitably stress crop production,and weather index insurance is one possible financial tool to mitigate such agro-metrological losses.In this study,we investigated where two rainfall-related weather indices—anomaly-based index(AI)and humidity-based index(HI)—could be best used for three main crops(rice,wheat,and maize)in China’s main agricultural zones.A county is defined as an“insurable county”if the correlation between a weather index and yield loss was significant.Among maize-cropping counties,both weather indices identified more insurable counties for deficit rain than for excessive rain(AI:172 vs 63;HI:182 vs 68);moreover,AI identified lower basis risk for deficit rain in most agricultural zones while HI for excessive rain.For rice,the number of AIinsurable counties was higher than the number of HI-insurable counties for deficit rain(274 vs 164),but lower for excessive rain(199 vs 272);basis risks calculated by two weather indices showed obvious difference only in Zone I.Finally,more wheat-insurable counties(AI:196 vs 71;HI:73 vs 59)and smaller basis risk indicate that both weather indices performed better for excessive rain in wheatplanting counties.In addition,most insurable counties showed independent yield loss,but did not necessarily result in effective risk pooling.This study is a primary evaluation of rainfall-related weather indices for the three main crops in China,which will be significantly helpful to the agricultural insurance market and governments’policy making.
基金This research was supported in part by the National Natural Science Foundation of China(72042015,72091211,72031006 and 71722006).
文摘Capacity planning is a very important global challenge in the face of Covid-19 pandemic.In order to hedge against the fluctuations in the random demand and to take advantage of risk pooling effect,one needs to have a good understanding of the variabilities in the demand of resources.However,Covid-19 predictive models that are widely used in capacity planning typically often predict the mean values of the demands(often through the predictions of the mean values of the confirmed cases and deaths)in both the temporal and spatial dimensions.They seldom provide trustworthy prediction or estimation of demand variabilities,and therefore,are insufficient for proper capacity planning.Motivated by the literature on variability scaling in the areas of physics and biology,we discovered that in the Covid-19 pandemic,both the confirmed cases and deaths exhibit a common variability scaling law between the average of the demand μ and its standard deviationσ,that is,σ ∝ μ^(β),where the scaling parameterμis typically in the range of 0.65 to 1,and the scaling law exists in both the temporal and spatial dimensions.Based on the mechanism of contagious diseases,we further build a stylized network model to explain the variability scaling phenomena.We finally provide simple models that may be used for capacity planning in both temporal and spatial dimensions,with only the predicted mean demand values from typical Covid-19 predictive models and the standard deviations of the demands derived from the variability scaling law.
