Department of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan, China.
PLoS One. 2024 Aug 2;19(8):e0306641. doi: 10.1371/journal.pone.0306641. eCollection 2024.
As the primary goal of the 17 Sustainable Development Goals (SDGs), poverty eradication is still one of the major challenges faced by countries around the world, and relative poverty is a comprehensive poverty pattern triggered by the superposition of economic, social, and environmental dimensions. Therefore, Therefore, this paper introduces the perspective of coupled coordination to consider the formation of relative poverty, constructs indicators in three major dimensions: economic, social, and environmental, proposes a fast and more accurate method of identifying relative poverty in a region by using machine learning, measures the degree of coupled coordination of China's relatively poor provinces using a coupled coordination model and analyzes the relationship with the level of relative poverty, and puts forward suggestions for poverty management on this basis using typology classification. The results of the study show that: 1) the fusion of data crawlers, remote sensing space, and other multi-source data to construct the dataset and propose a fast and efficient regional relative poverty identification method based on big data with low comprehensive cost and high identification accuracy of 0.914. 2) Currently, 70.83% of the economic-social-environmental systems of the relatively poor regions are in the dysfunctional type and are in a state of disordered development and malignant constraints. The regions showing coupling disorders are mainly clustered in the three southern prefectures of Xinjiang, Qinghai, Gansu, Yunnan, and Sichuan, and their spatial distribution is relatively concentrated. 3) The types of poverty and their coupled and coordinated development in each region show large spatial variability, requiring differentiated poverty eradication countermeasures tailored to local conditions to achieve sustainable regional economic-social-environmental development.
作为 17 个可持续发展目标（SDGs）的首要目标，消除贫困仍然是世界各国面临的主要挑战之一，而相对贫困是经济、社会和环境维度叠加引发的综合贫困模式。因此，本文引入耦合协调视角来考虑相对贫困的形成，构建经济、社会和环境三个主要维度的指标，提出一种利用机器学习快速准确识别区域相对贫困的方法，使用耦合协调模型衡量中国相对贫困省份的耦合协调程度，并分析与相对贫困水平的关系，在此基础上利用类型学分类提出贫困管理建议。研究结果表明：1）融合数据爬虫、遥感空间等多源数据构建数据集，提出一种基于大数据的快速高效区域相对贫困识别方法，具有综合成本低、识别准确率高（0.914）的特点。2）目前，相对贫困地区的经济-社会-环境系统中，有 70.83%处于非功能型，处于无序发展和恶性制约的混乱状态。表现出耦合失调的地区主要集中在新疆、青海、甘肃、云南、四川的三个南部州，其空间分布相对集中。3）各地区的贫困类型及其耦合协调发展呈现出较大的空间变异性，需要因地制宜制定有针对性的脱贫对策，实现区域经济-社会-环境的可持续发展。
