| 农户贫困脆弱性测度及其影响因素——基于秦巴山区的实证分析 |
| |
| 引用本文： | 刘倩,蒋金秀,杨星,张军以,杨新军.农户贫困脆弱性测度及其影响因素——基于秦巴山区的实证分析[J].地理研究,2022,41(2):307-324. |
| |
| 作者姓名： | 刘倩 蒋金秀 杨星 张军以 杨新军 |
| |
| 作者单位： | 1.重庆师范大学地理与旅游学院,重庆 4013312.三峡库区地表过程与环境遥感重庆市重点实验室,重庆 4013313.陕西省地表系统与环境承载力重点实验室,西安 7101274.西北大学城市与环境学院,西安 710127 |
| |
| 基金项目： | 国家自然科学基金项目（41771574、41901214）；;重庆市教委科技项目（KJQN202000524）； |
| |
| 摘 要： | 防止致贫返贫、建立脱贫长效机制是巩固拓展脱贫攻坚成果的关键落脚点。探究农户贫困脆弱性及其机制可为建立预防致贫返贫机制提供思路和借鉴。通过构建贫困脆弱性分析框架和测度体系,以秦巴山区为例,测度农户贫困脆弱性水平,分析贫困脆弱性差异,采用分位数回归模型揭示农户贫困脆弱性的影响因素。结果表明：① 农户贫困脆弱性水平均值为0.046,贫困脆弱性等级呈现“纺锤形”分布。② 农户贫困脆弱性水平及不同维度间差异明显。补贴依赖型、务农主导型农户受健康冲击或教育压力大且适应力薄弱,贫困脆弱性较高。多元型和纯务工型农户具有低风险与低敏感性,适应力较高,贫困脆弱性较低。③ 农户的暴露风险、适应力具有地域分异性,中山区农户自然风险较高且高贫困脆弱性的农户比例大;河谷川塬区农户的适应力较高。④ 建档立卡贫困识别与贫困脆弱性评估结果具有一定差异。⑤ 农户贫困脆弱性受家庭层面的户主受教育程度、健康水平、职业类型、社会连接度、政策依赖性、非农就业人数、生计多样性以及村域层面的地形起伏度、道路可达性、与河流的距离以及教育可及性等因素的影响。
|
| 关 键 词： | 贫困风险 贫困脆弱性 农户 分位数回归 秦巴山区 |
| 收稿时间： | 2020-11-27 |
Poverty vulnerability measurement and its impact factors of farmers:Based on the empirical analysis in Qinba Mountains |
| |
| LIU Qian,JIANG Jinxiu,YANG Xing,ZHANG Junyi,YANG Xinjun.Poverty vulnerability measurement and its impact factors of farmers:Based on the empirical analysis in Qinba Mountains[J].Geographical Research,2022,41(2):307-324. |
| |
| Authors: | LIU Qian JIANG Jinxiu YANG Xing ZHANG Junyi YANG Xinjun |
| |
| Institution: | 1. College of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China2. Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, Chongqing 401331, China3. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi'an 710127, China4. College of Urban and Environmental Sciences, Northwestern University, Xi'an 710127, China |
| |
| Abstract: | The prevention of poverty-returning and the building of a long-term mechanism for poverty alleviation are the keys to consolidating and expanding the achievements.Exploring the poverty vulnerability of rural households and its corresponding mechanism can provide ideas and examples for the establishment of a early warning mechanism.Taking Qinba Mountains as an example,this paper measured the poverty vulnerability level of rural households and explored their differentiation.The factors affecting poverty vulnerability were revealed by Quantile regression mode.(1)The analystical framework of poverty vulnerability was effective.In addition,the poverty vulnerability level of rural households was 0.046,showing a“spindle-shaped”distribution.(2)Poverty vulnerability and its different dimensions were characterized by differentiation.Subsidy-dependent and agricultural-dominated households showed high poverty vulnerability due to the high risks of health or education and weak adaptability.Diversified and pure-work farmers had low vulnerability to poverty because of their low risks and sensitivity and strong adaptability.(3)The exposure risks and adaptability of rural households were geographically differentiated.Rural households in the middle mountainous area exhibited high natural risks,with a large proportion of high-vulnerability households,while those in the river valley area displayed high adaptability.(4)A certain difference existed between the identification of poverty with archiving cards and the assessment of poverty vulnerability.(5)The poverty vulnerability of rural households was affected by the education level,health and occupation type of household heads,social connectivity,policy dependence,non-agricultural employment ratio and livelihood diversity at the household level,topographical fluctuations,road accessibility,as well as the distance from rivers,and schools at the village level. |
| |
| Keywords: | risk of poverty poverty vulnerability farmer Quantile regression Qinba Mountains |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《地理研究》浏览原始摘要信息 |
| 点击此处可从《地理研究》下载免费的PDF全文 |
|
