中文摘要：

提高贫困人口瞄准和识别的准确度是农村扶贫开发需要解决的首要问题，近年来从多个角度进行多维贫困的精准度量与分析成为国内外扶贫领域的研究热点。该研究以Sen的能力方法理论为基础，以Alkire和Foster的多维贫困指数（MPI）测算指标体系为参考，结合研究区实际情况，建立了生活水平维度贫困指标体系，利用武陵片北部11个县的贫困农户调查数据进行了基于“A—F”双临界值法的县级贫困人VI量算，并通过“维度加总一分解”算法进行了空间分异分析。结果显示：生活水平维度中通电与资产指标对研究区贫困发生率和多维贫困指数影响最小，燃料类型指标影响最大；11个县贫困程度由大到小依次为：巴东县〉石柱土家族自治县〉秭归县〉建始县〉来凤县〉恩施市〉鹤峰县〉利川市〉丰都县〉咸丰县〉黔江区；依据房屋结构、燃料类型、饮水情况3个指标可将11个县分为甲乙丙3种类型，不同类型县的主要致贫因素不同，当地政府应根据各县具体情况采取差异化扶贫政策。

英文摘要：

After 30 years of hard work, there is a significant reduction in China＇s rural poor problem. The poor family was usually identified when the family living level was lower than average income level in the past years. This traditional assessment method based on income level is easy and quick in helping the government to identify most poor people. But those people who are living in a low living quality or suffering from disease or have no chance to get well education but have higher income than the poverty level should be seemed as poor people too. And hence the traditional method was not suitable. Improving the accuracy of identifying the poors becomes the most im- portant mission for rural poverty alleviation and development in new stage. Accurate measurement and analysis of multidimensional poverty from multiple angles is a research hotspot at home and abroad in the field of poverty alleviation in recent years. Based on the Sen$ capability approach theory, and multidimensional poverty index system established by Alkire and Foster in 2008, which was used by UNDP to measure 104 countries in the Human Development Report in 2010, according to the actual situation of the study area, this paper established a poverty identifying index system of living quality dimension, which included 5 index, such as electricity, asset, safe water, house structure and fuel type. And then, combining with the survey data, it assessed the multidimensional poverty of the poor of 11 counties in the north of poverty - stricken areas at ＂ county - village＂ scale in Wuling, analyzed the spatial heterogeneity through the ＂ dimension aggregated/decomposition＂ . The result showed that Electricity and asset indexes had no effect on incidence of poverty and multidimensional poverty level but fuel index in the study area. The 11 counties in descending order of poverty levels were as follows ： Badong County 〉 Shizhu Tujia Autonomous County 〉 Zigui County 〉 Jianshi County 〉 Laifeng County 〉 Enshi City 〉 Hefeng County 〉 Lichua