中文摘要：

作为国家扶贫开发决策实施的重要单元,贫困县贫困程度及其致贫原因的识别和评估是国家＂精准扶贫＂战略实施的前提和保障.本文从生态贫困的视角,设计了顾及自然环境-经济-社会可持续协调发展的县级别多维贫困度量指标体系,构建基于贫困指数-最小方差模型（PI-MVM）的县级多维贫困度量模型,以6个连片特困区的249个县为典型研究区,系统揭示片区-县级层面上的贫困程度、致贫原因及其空间分布特征.结果显示：各片区的综合贫困程度由北向南逐渐加重,各片区县存在＂从北向南、从东到西,贫困程度逐渐加重＂的趋势;乌蒙片区西部、秦巴片区西北部各县贫困程度的高-高聚集现象突出;秦巴中南部以及乌蒙片区受自然环境因素影响较大,贫困程度较深.一般致贫型片区县较多,主导致贫型片区县聚集在贫困程度较低的片区;经济因素对贫困的缓解作用逐渐下降,自然环境、社会发展因素的影响逐渐明显.研究结果可以更加精准地全面把握贫困县的贫困区划特征,为指导研究区早日脱贫提供辅助决策技术支撑.

英文摘要：

As an important implementation unit of national poverty alleviation and development strategies,identifying each poverty-stricken county＇s poverty degree and poverty contributing factors is the precondition and guarantee of implementing national precise poverty reduction strategies.To response to it,from the perspective of ecological poverty,this paper brought up a county-level multidimensional poverty measurement index system,which took into considerations of the sustainable development among natural environment,economy and society,and then constructed a PI-MVM multidimensional poverty measurement model to explore the destitute areas and poverty-stricken counties＇ poverty degrees and poverty contributing factors,as well as their spatial distributions.The case test from 6 destitute areas and 249 counties shows that the poverty degrees of the destitute areas increase from north to south,and the counties＇ poverty degrees present a trend of ＂increasing from north to south and from east to west＂.There exists a high-high aggregation distribution both in west of Wumeng and northwest of Qinba areas.Natural environment factors play an important role in both south central of Qinba and Wumeng area,where the poverty degree is higher.Those counties with general poverty contributing type have the most cases,while those counties with dominant po-verty contributing type are gathered in smaller poverty-degree areas.The contribution of economic factors to poverty alleviation decreases gradually.On the contrary,the influences of natural environment and society factors increase obviously.This result could help policy-makers to grasp the whole poverty characteristic of poverty-stricken counties,as well as provide technical support for auxiliary decision making.