%0 Journal Article
%T 贫困退出背景下衡阳返贫脆弱性调查与评价<br>Investigation and Evaluation of Hengyang’s Vulnerability to Poverty under the Background of Poverty Withdrawal
%A 周宇晴
%A 黄妹珍
%A 陶闯
%A 杨涵媛
%A 黄飞
%J Sustainable Development
%P 1891-1898
%@ 2160-7559
%D 2024
%I Hans Publishing
%R 10.12677/sd.2024.147218
%X 针对区域返贫脆弱性进行精确识别，预防和化解返贫风险成为“后扶贫时代”的核心任务。本文基于暴露–敏感–适应理论框架，构建生态–福利–经济三位一体的返贫脆弱性指标体系，以2021年的数据为样本，采用BP神经网络，科学评估衡阳7个县域返贫脆弱性问题，研究发现：1) 常宁、衡山与衡阳整体返贫脆弱度高，处于最高级别；衡东、衡南与耒阳整体返贫脆弱度低，处于最低级别；2) 根据主导要素的差异，将衡阳返贫脆弱性较高的地区划分为三大类别，其中衡山县属于多维约束返贫脆弱县，衡阳县表现为生态–福利约束返贫脆弱县，而常宁则为生态–经济约束返贫脆弱县；3) 为有效预防和化解返贫，需要协同薄弱区域的人、地和业要素，重视生态环境与贫困的相互影响，加强政策干预。<br />
Accurately identifying regional vulnerability to poverty relapse, preventing and resolving the risk of poverty relapse have become the core tasks of the “post-poverty alleviation era”. Based on the exposure-sensitivity-adaptation theoretical framework, this paper constructs a trinity vulnerability index system for poverty relapse that integrates ecology, welfare, and economy. Using data from 2021 as a sample and the BP neural network, this paper scientifically assesses the vulnerability to poverty relapse in seven counties in Hengyang. The research findings are as follows: 1) Changning, Hengshan, and Hengyang as a whole have high vulnerability to poverty relapse, ranking at the highest level; Hengdong, Hengnan, and Leiyang have low vulnerability to poverty relapse, ranking at the lowest level; 2) According to the differences in dominant factors, the regions with high vulnerability to poverty relapse in Hengyang are divided into three categories. Among them, Hengshan County belongs to a county with multidimensional constraint vulnerability to poverty relapse, Hengyang County exhibits vulnerability to poverty relapse constrained by ecology and welfare, while Changning is vulnerable to poverty relapse constrained by ecology and economy; 3) To effectively prevent and resolve poverty relapse, it is necessary to coordinate the human, land, and industrial factors in vulnerable regions, pay attention to the mutual influence between the ecological environment and poverty, and strengthen policy interventions.
%K 脱贫，返贫脆弱性，衡阳，BP神经网络<br>Get Rid of Poverty
%K Vulnerability to Return to Poverty
%K Hengyang
%K BP Neural Network
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=92710