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作 者:杨奉海 谢钊[1] 王朝晖 杨国永[1,2] YANG Feng-hai;XIE Zhao;WANG Zhao-hui;YANG Guo-yong(School of Public Administration and Law,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;Fujian Rural Development Think Tank,Fuzhou,Fujian 350002,China)
机构地区:[1]福建农林大学公共管理与法学院,福建福州350002 [2]福建农村发展智库,福建福州350002
出 处:《台湾农业探索》2023年第5期26-34,共9页Taiwan Agricultural Research
基 金:国家社会科学基金项目(20BGL240)。
摘 要:【目的/意义】农村人口空心化是导致农村经济衰退的直接原因,研究农村人口空心化,以期为抑制农村人口空心化程度、实施针对性治理措施提供科学依据。【方法/过程】以福建省55个县(含县级市)为研究区域,选取2000、2010和2020年3个年份,运用熵值法、空间自相关、标准差椭圆、GWR模型对福建省农村人口空心化时空演变特征和影响因素进行探究。【结果/结论】结果显示:福建省农村人口空心化程度不断加深,但农村人口空心化空间相关性逐渐减弱。农村人口空心化热点区逐渐向西部山区聚集,而冷点区逐渐向西北、东部区域分散。人均农作物播种面积、人均居民点面积和平均坡度加剧了农村人口空心化程度,而人均GDP对农村人口空心化具有抑制作用,同时不同影响因素对不同区域的影响程度具有明显的差异性。【Objective/Meaning】The rural population hollowing is the direct cause of rural economic recession.The research on the rural population hollowing was expected to provide scientific basis for restraining the degree of rural population hollowing and implementing the targeted governance measures.【Methods/Procedures】By taking 55 counties(including the county-level cities)in Fujian Province as the research area,the three years(2000,2010 and 2020) were selected,and the entropy method,spatial autocorrelation,standard deviation ellipse and GWR model were used to explore the characteristics and influencing factors of the spatial and temporal evolution of rural population hollowing in Fujian Province.【Results/Conclusions】The results showed that the degree of rural population hollowing in Fujian Province was deepening continuously,but the spatial correlation of rural population hollowing was gradually weakening.The hot spots of rural population hollowing gradually converged to the western mountainous areas,while the cold spots gradually dispersed to the northwest and eastern regions.The per capita crop sown area,per capita residential area and average slope have aggravated the hollowing degree of rural population,while per capita GDP had an inhibitory effect on the hollowing of rural population,and the influence degree of different influencing factors on different regions had an obvious difference.
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