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基于夜间灯光数据的西南地区县域旅游多维减贫效应时空变化研究 被引量:4

The Spatio-temporal Changes of Tourism Multidimensional Poverty Alleviation Effect at County in Southwest China based on Night Light Data
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摘要 旅游发展是消除贫困的重要途径之一,从多维贫困视角对旅游减贫进行研究具有重要意义。以脱贫摘帽的西南地区国家级贫困县为研究案例,引入NPP/VIIRS夜间灯光数据测度多维贫困,并使用地理加权回归方法分析2012—2019年旅游发展的多维减贫效应。结果表明:(1)2012—2019年,西南地区贫困县旅游发展水平不断提高,呈现“东高西低”的空间格局,旅游发展的区域差异不断缩小;(2)2012年以来,西南地区贫困县多维贫困日趋改善;空间分布上,多维贫困程度自西向东逐渐降低,空间差异趋于缩小;(3)西南地区贫困县旅游发展能够缓解包括经济、教育、医疗和社会保障等方面的多维贫困,旅游多维减贫效应具有明显的空间异质性,川东、川北和滇东南地区贫困县的旅游减贫强度较高,滇西、渝东南和黔西地区旅游减贫强度较低;(4)旅游减贫强度主要受到旅游资源禀赋、经济发展水平和交通区位条件的影响,影响因素通过旅游资源开发、强化旅游产业支撑和提高旅游通达性等途径改善贫困地区的经济、教育、医疗和社会保障水平,实现旅游发展的多维减贫。旅游减贫的发展需要结合贫困地区实际情况考虑,提高旅游产品竞争力,完善配套设施建设,改善交通运输条件,以提高旅游减贫效果。另外,研究发现基于夜间灯光数据测度多维贫困具有较高的精度,能够为贫困问题研究提供数据支持。研究结果对中国旅游扶贫战略实施尤其是2020年后预防返贫具有重要的参考价值。 Tourism development is one of the important ways to eradicate poverty.It is of great significance to study tourism poverty alleviation effect from the perspective of multidimensional poverty.In this study,we take the national-level poverty-stricken counties of the Southwest China region that have been lifted out of poverty as the study cases,and introduce the NPP/VIIRS night light data to measure multidimensional poverty.The Geographically Weighted Regression method is used to explore the multidimensional poverty reduction effect of tourism development from 2012—2019.The results show that:(1)From 2012 to 2019,the level of tourism development in poor counties in Southwest China continued to improve,showing a spatial pattern of"high in the east and low in the west",and the regional difference in tourism development decreased;(2)In the past eight years,multidimensional poverty in poor counties in Southwest China was alleviated steadily.In terms of spatial distribution,the level of multidimensional poverty gradually decreased from the west to the east,and the spatial difference tended to decrease;(3)The tourism development can alleviate multidimensional poverty in economic,educational,medical,and social security aspects at county level.There is obvious spatial heterogeneity in the intensity of tourism multidimensional poverty reduction,with Sichuan’s poor counties having the highest intensity of tourism poverty reduction.Overall,the tourism poverty reduction intensity is higher in poor counties in eastern Sichuan,northern Sichuan,and southeastern Yunnan,and lower in western Yunnan,southeastern Chongqing,and western Guizhou;(4)The intensity of tourism poverty reduction is mainly influenced by the endowment of tourism resource,the level of economic development,and the location of transportation.These influencing factors promote tourism development through the development of tourism resource,the strengthening of tourism industry support,and the improvement of tourism accessibility,etc.The tourism industry has a strong industrial linkage that leads to comprehensive regional development,improving economic,educational,medical,and social security levels in poor areas,and achieving multidimensional poverty reduction through tourism development.The intensity of tourism poverty reduction in poor counties is influenced by a variety of factors,so tourism poverty reduction development needs to be considered in the context of the actual situation.The effectiveness of tourism in reducing poverty can be improved by increasing the competitiveness of tourism products and improving transport conditions.In addition,the study finds that the measurement of multidimensional poverty based on night light data has a high accuracy and can provide data support for poverty research.The findings of the study have important reference value for the implementation of China’s tourism poverty alleviation strategy,especially for the prevention of poverty return after 2020.
作者 张颖 汪侠 闫艺涵 史舒悦 海少琪 ZHANG Ying;WANG Xia;YAN Yihan;SHI Shuyue;HAI Shaoqi(School of Geographic and Oceanographic Sciences,Nanjing University,Nanjing 210023,China)
出处 《地球信息科学学报》 CSCD 北大核心 2022年第8期1541-1557,共17页 Journal of Geo-information Science
基金 国家自然科学基金项目(41871134)。
关键词 旅游发展 多维贫困 夜间灯光数据 时空变化 影响机制 贫困县 西南地区 tourism development multidimensional poverty night light data spatio-temporal variation influencing mechanism poverty-stricken county Southwest China
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