Based on survey data of nine poverty-stricken counties in Wumen mountainous area,using the ordinal Probit model,this paper analyzed the influence mechanism for the assessment of the implementation effect of the policy...Based on survey data of nine poverty-stricken counties in Wumen mountainous area,using the ordinal Probit model,this paper analyzed the influence mechanism for the assessment of the implementation effect of the policy of relocating the poor. It obtained the following results.( i) Material security and social environment have a positive influence on the assessment of the policy of relocating the poor.( ii) In terms of material security,there is no significant relationship between the employment environment and the assessment of the policy of relocating the poor. Per capita annual income,per capita housing,income-increasing channels,and endowment insurance have significant effects on the assessment of the policy of relocating the poor.( iii) In social environment,public services,infrastructure,neighborhood relations,and environmental sanitation have significant effects on the assessment of the policy of relocating the poor,while there is no significant relationship between the awareness of poverty alleviation policies and the effect assessment of the policy of relocating the poor.( iv) The severer the poverty,the more significant the assessment of the implementation effect of the policy of relocating the poor. In accordance with the above results,it came up with recommendations for targeted poverty alleviation.展开更多
It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in dat...It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.展开更多
基金Supported by National Social Science Fund "Study on the Migration of Rural Climate Poverty Population in Frequent Climate Disaster Areas"(12BRK020)
文摘Based on survey data of nine poverty-stricken counties in Wumen mountainous area,using the ordinal Probit model,this paper analyzed the influence mechanism for the assessment of the implementation effect of the policy of relocating the poor. It obtained the following results.( i) Material security and social environment have a positive influence on the assessment of the policy of relocating the poor.( ii) In terms of material security,there is no significant relationship between the employment environment and the assessment of the policy of relocating the poor. Per capita annual income,per capita housing,income-increasing channels,and endowment insurance have significant effects on the assessment of the policy of relocating the poor.( iii) In social environment,public services,infrastructure,neighborhood relations,and environmental sanitation have significant effects on the assessment of the policy of relocating the poor,while there is no significant relationship between the awareness of poverty alleviation policies and the effect assessment of the policy of relocating the poor.( iv) The severer the poverty,the more significant the assessment of the implementation effect of the policy of relocating the poor. In accordance with the above results,it came up with recommendations for targeted poverty alleviation.
基金The Innovation Program of Shanghai Municipal Education Commission under contract No.14ZZ147the Opening Project of Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources(Shanghai Ocean University),Ministry of Education under contract No.A1-0209-15-0503-1
文摘It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.
