摘要
连片特困区贫困是特殊的区域性贫困,实现持久脱贫,关键在于识别和评估区域的贫困程度以及深究其致贫原因。基于此,通过构建经济维度硬现状、社会维度软现状及自然维度潜在状态3维的多维贫困度量指标体系,应用均方差权重法测算了2006、2010及2014年秦巴集中连片特困区及各子区域的多维贫困度,并分析了多维贫困及其各维度的时空演变规律。结果表明:(1)秦巴集中连片特困区多维贫困度得分偏高,表明该区域贫困程度较深,且多维贫困在不同贫困维度上呈现显著差异。(2)2006—2014年秦巴集中连片特困区多维贫困度都呈减弱趋势,且表现出以甘肃省徽县至湖北省房县为轴带的中部地区集聚的空间演变趋势。(3)在整体多维贫困显著改善的同时,不同连片扶贫区及不同的贫困维度在减贫路径上也体现出不同的演化特征。所得结论为该区域内县域尺度贫困类型特征识别以及扶贫工作前期评估以及后期验收成效等提供一定技术支持,也为后期实施因地制宜的脱贫措施提供科学的辅助决策支撑。
Background,aim,and scope Since the beginning of the 21st century,China has made great progress in poverty alleviation.The incidence rate of poverty has decreased from 17.2%in 2010 to 5.7%in 2015,with an average annual poverty reduction of 21.98 million people.However,the evaluation of poverty focuses on single indicator,like income or consumption,which is obviously not accurate enough.As a result,it is of great necessity to build a multidimensional evaluation system.Poverty in contiguous destitute areas is a special regional poverty.Accurate identification and evaluation poverty degree,exploring the causes of poverty are the premises of lasting poverty reduction.Qinba contiguous destitute areas cover five provinces,including Henan,Hubei,Sichuan,Shaanxi,Gansu,and Chongqing municipality.It integrates old revolutionary base areas,large reservoir areas and natural disaster-striken areas.The complex internal differences and various poverty factors make these areas the most important areas in China’s new round of poverty alleviation and development.Materials and methods Based on those,the paper shows the multidimensional poverty index system(MPI)with three dimensions,including the hard state of the economic dimension(ED),the soft state of the social dimension(SD)and the potential state of the natural dimension(ND).Then,it estimates MPI of all the counties by standard variance method in 2006,2010 and 2014,and analyzes their spatio-temporal evolution.Results The results showed that the MPI in the povertystricken areas of Qinling-Dabashan region were high,which indicates that the poverty degree in this region were severe.The MPI varies in different poverty dimensions.In the time period of 2006 to 2014,the poverty degree showed a decreasing trend,and showed the spatial trend of agglomeration in the central region from Huixian County(Gansu Province)to Fangxian County(Hubei Province).While the overall MPI were significantly improved,the different contiguous poverty regions and different poverty dimensions also showed different evolutionary characteristics in poverty reduction paths.Discussion Based on the multidimensional poverty index system,the regional poverty of Qinba contiguous destitute areas can accurately reflect the poverty situation.However,due to the limitation of data acquisition,the county poverty characteristics were only measured based on the cross-section data of three time periods in Qinba contiguous destitute areas.Conclusions In Qinba contiguous destitute areas,the county multidimensional poverty was high volatile and varies in different poverty dimensions.Economic and social infrastructure were the main cause of poverty.Recommendations and perspectives The conclusions can provide some technical support such as the identification poverty types of the county-level,the early assessment of the poverty alleviation work and the post-acceptance effect in this region.It also provides scientific auxiliary decision-making support for the late implementation of the policy of poverty alleviation.
作者
安彬
肖薇薇
段塔丽
AN Bin;XIAO Weiwei;DUAN Tali(School of Tourism&Environment,Ankang University,Ankang 725000,China;Engineering Technology Research Center for Water Resource Protection and Utilization of Hanjiang River,Ankang 725000,China;Shaannan Eco-economy Research Center,Ankang 725000,China;School of Philosophy and Government,Shaanxi Normal University,Xi’an 710119,China)
出处
《地球环境学报》
CSCD
2018年第5期508-520,共13页
Journal of Earth Environment
基金
陕西省高校科协青年人才托举计划项目
陕西省社会科学基金重点项目(2016G001)
大学生创新创业训练计划项目(2017akxy042)~~
关键词
秦巴集中连片特困区
多维贫困
空间格局
均方差赋权法
Qinba contiguous destitute areas
multidimensional poverty
spatial distribution
standard variance method
