Temporal/Spatial Evolution and Vulnerability of Return-to-Poverty After Successful Implementation of Alleviation Program in Fujian
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摘要:
目的 当前,中国扶贫工作取得了显著成效,2020年现行标准下农村贫困人口将全面脱贫。在此背景下,伴随着高脱贫率的返贫现象引起了政府和学术界的高度关注,然而目前鲜有揭示返贫脆弱性时空格局的研究成果。论文探究福建省67个县(市、区)在2012-2016年期间的返贫脆弱性的时空分异与产生机制,为精准识别重点扶贫对象、制定针对性强的有效措施提供科学参考。 方法 论文从暴露性、敏感性和适应能力3个维度构建多维返贫脆弱性评价指标体系,并基于地理学视角与GIS技术手段进行返贫脆弱性测度和可视化表达。 结果 (1)福建省的返贫脆弱性程度整体降低,不同返贫脆弱性等级呈现不同的态势,高返贫脆弱性从65.7%下降至31.8%,中返贫脆弱性从23.9%提高至56.1%,低返贫脆弱性从10.4%提高至12.1%。(2)其空间范围也具有明显的动态变化。高返贫脆弱性在空间上表现为"破碎-连接",中返贫脆弱性表现为"集聚-连片",低返贫脆弱性总体呈现"收缩-连接"态势;(3)第一产业增长值、固定资产投资额、人均土地面积、农村用电量、卫生机构床位数和农村居民最低生活保障人数比例与返贫脆弱性显著相关。 结论 福建省的返贫脆弱性具有时间和空间维度的动态性,中、高返贫脆弱性普遍存在,低返贫脆弱性仅集中在部分沿海都市区,且返贫脆弱性的产生机制具有多维特征。未来实现可持续稳定脱贫,要重点关注中、高返贫脆弱性地区,将脆弱性等级识别和精准举措紧密对应,并加强返贫预警。 Abstract:Objective The temporal/spatial evolution and vulnerability to reversal of the remarkably accomplished poverty alleviation program in China were analyzed using the experience in Fujian as a reference to the national goal of total poverty eradication in the country by 2020. Method A multi-dimensional vulnerability evaluation system embracing the aspects of exposure, sensitivity, and adaptability of economically destitute population to poverty was established. And, based on the geographical perspective using the GIS technology, data and visual presentation were made available to analyze the vulnerability of 67 counties and cities in the province from 2012 to 2016 in returning to poverty after successful implementation of the alleviation program. Result (1) Over all, the vulnerability to the reoccurrence of economic destitution in the surveyed areas lessened in those 4 years. A significant decline occurred in the high vulnerability category, from 65.7% to 31.8%, although the moderate vulnerability segment increased from 23.9% to 56.1%, and the low vulnerability rose from 10.4% to 12.1%. (2)There were significant dynamic spatial changes taken place during the same period as well. In terms of space, the high vulnerability category was of "fragmentation-connection" type, the moderate vulnerability, "agglomeration-continuous", and the low vulnerability, "contraction-connection." (3) There were significant correlations between the vulnerability and the value increase of primary industry, investment in fixed assets, per capita land area, electricity consumption in rural regions, number of beds in health facilities, and proportion of farming population living below the minimum standard. Conclusion The vulnerability of the poor in the regions in Fujian to reverse back into destitution after being lifted out of poverty still existed, both temporally and spatially, despite the alleviation efforts. However, it was more commonly observed at the high and moderate categories, and, at the low level, only in some coastal metropolitan cities.The roots and mechanism behind the reversing outcome were multi-dimensional. To achieve a sustainable result, efforts would necessarily be focused on localities of high and moderate vulnerability categories to implement measures specifically designed for the identified deficiencies with an early warning system to prevent the return of poverty. -
Key words:
- poverty /
- return-to-poverty /
- vulnerability /
- poverty alleviation /
- Fujian Province
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表 1 福建省县域多维返贫脆弱性指标体系及权重分配
Table 1. Multi-dimensional vulnerability evaluation system and weight distribution of return-to-poverty incidents in counties of Fujian
维度层
Dimension level类别层
Class level指标层
Index level暴露度
Exposure(0.3)自然环境(0.6) 自然灾害程度(0.4) 森林覆盖程度(0.6) 社会风险(0.4) 城镇登记失业率(1) 敏感性
Sensitivity(0.3)人口结构(0.4) 老年人口比例(1) 经济结构(0.6) 第一产业增长值(0.7) 固定资产投资额(0.3) 适应能力
Adaptation(0.4)金融资本(0.2) 农民人均纯收入(1) 人力资本(0.4) 普通初中在校生比例(0.5) 15~64岁人口比例(0.5) 自然资本(0.1) 人均土地面积(1) 物质资本(0.1) 公路通车里程(0.6) 农村用电量(0.4) 社会资本(0.2) 卫生机构床位数(0.3) 城镇化率(0.3) 农村居民最低生活保障人数比例(0.4) 表 2 各类别与返贫脆弱性得分的Pearson相关分析的显著性
Table 2. Pearson correlation significances between categories and scores on vulnerability of return-to-poverty
年份
Year自然环境
Natural environment社会风险
Social risk人口结构
Population structure经济结构
Economic structure金融资本
Financial capital人力资本
Human capital自然资本
Natural capital物质资本
Physical capital社会资本
Social capital2012 0.006* 0.067 0.000* 0.000* 0.000* 0.110 0.000* 0.000* 0.000* 2014 0.403 0.011* 0.007* 0.000* 0.000* 0.849 0.044* 0.000* 0.000* 2016 0.586 0.027* 0.000* 0.000* 0.000* 0.381 0.000* 0.000* 0.000* 注:*在0.05水平上显著相关(R2<0.05)。表 3同。
Note:* Significantly correlated at 0.05 (R2<0.05).The same as Table 3.表 3 不同指标与返贫脆弱性得分的线性回归结果
Table 3. Linear regression between indicators and scores on vulnerability of return-to-poverty
项目
Item人口结构 经济结构 金融资本 自然资本 物质资本 社会资本 老年人口比例 第一产业增长值 固定资产投资额 农民人均纯收入 人均土地面积 公路通车里程 农村用电量 卫生机构床位数 城镇化率 农村居民最低生活保障人数比例 显著性
Significance0.096 0.000* 0.028* 0.722 0.000* 0.486 0.000* 0.037* 0.052 0.000* 相关系数
Correlation coefficient0.081 -0.205 -0.107 0.027 -0.352 -0.019 -0.054 -0.113 -0.271 -0.161 注:人口结构 Population structure,经济结构 Economic structure,金融资本 Financial capital,自然资本 Natural capital,物质资本 Physical capital,社会资本 Social capital,老年人口比例 Proportion of elderly population,第一产业增长值 Growth of primary industry,固定资产投资额 Fixed investments,农民人均纯收入 The per capita net income of farmers,人均土地面积 Per capita land area,公路通车里程 Highway mileage,农村用电量 Rural electricity consumption,卫生机构床位数 Number of beds in health facilities,城镇化率 Urbanization rate,农村居民最低生活保障人数比例 The proportion of rural residents receiving minimum living allowances。 -
[1] UNITED STATES. The Millennium Development Goals Report[M]. United States, 2015: 16-25. [2] 国家统计局.国民经济和社会发展统计公报[R/OL]. (2013-02-22/2019-02-28)[2019-06-05].http://www.stats.gov.cn/tjsj/zxfb. [3] 中华人民共和国农业农村部.聚力精准施策, 决战决胜脱贫攻坚[R/OL]. (2019-02-20)[2019-06-05].http://www.moa.gov.cn/ztzl/jj2019zyyhwj. [4] 刘永富.以精准发力提高脱贫攻坚成效[N].人民日报, 2016-01-11: B7.LIU Y F. Targeted efforts to improve poverty alleviation efforts[N]. The People's Daily, 2016-01-11: B7.(in Chinese) [5] 邓永超.乡村振兴下精准扶贫中防治返贫的优化机制[J].湖南财政经济学院学报, 2018, 35(4):49-56. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0120181201009968DENG Y C. Optimizing the Mechanism of Prevent and Control the Return of Poverty in Targeted Poverty Alleviation under Rural Revitalization[J]. Journal of Hunan University of Finance and Economics, 2018, 35(4):49-56.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0120181201009968 [6] 杨立雄.高度重视扶贫攻坚中的返贫问题[J].中国民政, 2016, 33(5):18-20. http://d.old.wanfangdata.com.cn/Periodical/zhongguomz201605006YANG L X. Great importance attach to re-poverty in poverty alleviation[J]. China Civil Affairs, 2016, 33(5):18-20.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/zhongguomz201605006 [7] 张珺.当前我国农村返贫现状与问题分析[J].中国管理信息化, 2011, 7(11):26-27. http://d.old.wanfangdata.com.cn/Periodical/zgkjdsh201111015ZHANG J. Current situation and problem analysis of rural re-poverty in China[J]. China Management Informationization, 2011, 7(11):26-27.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/zgkjdsh201111015 [8] 关孔春.精准扶贫中返贫的原因及其对策分析[J].劳动保障世界, 2018, 31(21):29-31. http://d.old.wanfangdata.com.cn/Periodical/ldbzsj201821018GUANG K C. Analysis on the causes and countermeasures of re-poverty in targeted poverty alleviation[J]. Labor Security World, 2018, 31(21):29-31.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/ldbzsj201821018 [9] 郑瑞强, 曹国庆.脱贫人口返贫:影响因素、作用机制与风险控制[J].农林经济管理学报, 2016, 15(6):619-624. http://d.old.wanfangdata.com.cn/Periodical/jxnydxxb-shkxb201606001ZHENG R Q, CAO G Q. Re-poverty Population:Influencing Factors, Mechanism and Risk Control[J]. Journal of Agro-Forestry Economics and Management, 2016, 15(6):619-624.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/jxnydxxb-shkxb201606001 [10] 姚建平, 王硕, 刘晓东.农业产业化的农户返贫风险研究——基于甘肃省靖远县两村庄枣农的分析[J].华北电力大学学报(社会科学版), 2017, 25(1):73-79. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hbdldxxb-shkxb201701012YAO J P, WANG S, LIU X D. The Risk of Poverty-return on Agriculture Industrialization:An Analysis Based on the Jujube Farmers of Two Villages in Jingyuan County of Gansu Province[J]. Journal of North China Electric Power University (Social Sciences Edition), 2017, 25(1):73-79. (in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hbdldxxb-shkxb201701012 [11] 刘小鹏, 苏胜亮, 王亚娟, 等.集中连片特殊困难地区村域空间贫困测度指标体系研究[J].地理科学, 2014, 34(4):447-453. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlkx201404008LIU X P, SU S L, WANG Y J, et al. The Index System of Spatial Poverty of Village Level to Monitor in Concentrated Contiguous Areas with Particular Difficulties[J].Scientia Geographica Sinica, 2014, 34(4):447-453.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlkx201404008 [12] 刘小鹏, 李永红, 王亚娟, 等.县域空间贫困的地理识别研究——以宁夏泾源县为例[J].地理学报, 2017, 72(3):545-557. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlxb201703014LIU X P, LI Y H, WANG Y J, et al. Geographical identification of spatial poverty at county scale[J]. Acta Geographica Sinica, 2017, 72(3):545-557.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlxb201703014 [13] 陈姝睿.基于GIS和BP的原州区空间贫困及其分异机制研究[D].宁夏: 宁夏大学, 2014.CHEN S R. Spatial Poverty and the Differentiation Mechanism of Yuanzhou based on GIS and BP Neural Network[D]. Ningxia: Ningxia University, 2014.(in Chinese) [14] 许月卿, 李双成, 蔡运龙.基于GIS和人工神经网络的区域贫困化空间模拟分析——以贵州省猫跳河流域为例[J].地理科学进展, 2006, 25(3):79-85. http://d.old.wanfangdata.com.cn/Periodical/dlkxjz200603010XU Y Q, LI S C, CAI Y L. Spatial Simulation Using GIS and Artificial Neural Network for Regional Poverty-A Case Study of MaotiaoheWatershed, Guizhou Province[J]. PROGRESS IN GEOGRAPHY, 2006, 25(3):79-85.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/dlkxjz200603010 [15] 曾永明, 张果.基于GIS和BP神经网络的区域农村贫困空间模拟分析——一种区域贫困程度测度新方法[J].地理与地理信息科学, 2011, 27(2):70-75. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlxygtyj201102016ZENG Y M, ZHANG G. Spatial Simulating in Regional Rural Poverty Based on GIS and BP Neural Network:A New Appraisement Method on Regional Rural Poverty[J]. Geography and Geo-Information Science, 2011, 27(2):70-75.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlxygtyj201102016 [16] 李鹤, 张平宇, 程叶青.脆弱性的概念及其评价方法[J].地理科学进展, 2008, 27(2):18-25. http://d.old.wanfangdata.com.cn/Periodical/dlkxjz200802003LI H, ZHANG P Y, CHENG Y Q. Concepts and Assessment Methods of Vulnerability[J]. Progress in Geography, 2008, 27(2):18-25.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/dlkxjz200802003 [17] WORLD BANK. World development Report 2000/2001 Attacking Poverty[M]. New York Oxford University Press, 2001:276-280. [18] 郑浩.贫困陷阱: 风险、人力资本传递和脆弱性[D].武汉: 武汉大学, 2012.ZHENG H. Poverty Traps: Risk, Human Capital Transition and Vulnerability[D]. Wuhan: Wuhan University. 2012.(in Chinese) [19] 蔡进, 禹洋春, 骆东奇, 等.重庆市农村多维贫困空间分异及影响因素分析[J].农业工程学报, 2018, 34(22):235-245. http://d.old.wanfangdata.com.cn/Periodical/nygcxb201822030CAI J, YU Y C, LUO D Q, et al. Space differentiation and its influence factor analysis of rural multidimensional poverty in Chongqing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(22):235-245.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/nygcxb201822030 [20] 福建省人民政府.省情概况[R/OL]. (2019-02-25)[2019-06-05]. http://www.fujian.gov.cn/szf/gk. [21] 福建省统计局.福建省国民经济和社会发展统计公报[R/OL]. (2019-02-28)[2019-06-05]. http://tjj.fujian.gov.cn/xxgk/tjgb. [22] 陆珍, 张明慧, 程煜等.福建省县域多维贫困空间分异研究[J].福建农林大学学报(哲学社会科学版), 2019, 22(2):19-25. http://d.old.wanfangdata.com.cn/Periodical/fjnydxxb-shkx201902003LU Z, ZHANG M H, CHENG Y, et al. Spatial differentiation of multidimensional poverty in county areas of Fujian[J]. Journal of Fujian Agriculture and Forestry University (Philosophy and Social Sciences), 2019, 22(2):19-25.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/fjnydxxb-shkx201902003 [23] 黄欣乐.福建省贫困人口分布、区域差异及扶贫机制研究[D].福州: 福建农林大学. 2016.HUANG X Y. The Study on The Distribution of The Poor, The Regional Differences and The Assistance Mechanism in Fujian Province[D]. Fuzhou: Fujian Agriculture and Forestry University, 2016.(in Chinese) [24] 福建省人民政府. 2019年福建省人民政府工作报告[R/OL]. (2019-01-21)[2019-06-11].http://www.fujian.gov.cn/szf/gzbg/szfgzbg. [25] 辛闻, 王虔, 中国网·中国扶贫在线.全球减贫伙伴研讨会: 以技术创新推动中国经验国际落地[R]. (2019-05-16)[2019-06-05].http://f.china.com.cn/2019-05/16/content_74790364.htm. [26] 何华征, 盛德荣.论农村返贫模式及其阻断机制[J].现代经济探讨, 2017, 36(7):95-102. http://d.old.wanfangdata.com.cn/Periodical/xdjjtt201707013HE H Z, SHENG D R. On the model of re-poverty in rural areas and its blocking mechanism[J]. Modern Economic Research, 2017, 36(7):95-102.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/xdjjtt201707013 [27] 韩峥.脆弱性与农村贫困[J].农业经济问题, 2004, 40(10):8-12. http://d.old.wanfangdata.com.cn/Periodical/nyjjwt200410002HAN Z. Fragility and Rural Poverty[J]. Issues in Agricultural Economy, 2004, 40(10):8-12.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/nyjjwt200410002 [28] 黄晓军, 王晨, 胡凯丽.快速空间扩张下西安市边缘区社会脆弱性多尺度评估[J].地理学报, 2018, 73(6):18-33. http://d.old.wanfangdata.com.cn/Periodical/dlxb201806002HUANG X J, WANG C, HU K L. Multi-scale assessment of social vulnerability to rapid urban expansion in urban fringe:Acase study of Xi'an[J]. Acta Geographica Sinica, 2018, 73(6):18-33.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/dlxb201806002 [29] WAGLE U R. The Counting-Based Measurement of Multidi-mensional Poverty:The Focus on Economic Resources, Inner Capabilities, and Relational Resources in the United States[J]. Social Indicators Research, 2014, 115(1):223-240. doi: 10.1007/s11205-012-0216-4 [30] 王少娜, 董瑞, 谢晖, 等.德尔菲法及其构建指标体系的应用进展[J].蚌埠医学院学报, 2016, 41(5):695-698. http://d.old.wanfangdata.com.cn/Periodical/bbyxyxb201605049WANG S N, DONG R, XIE H, et al. The application progress of Delphi method and its construction index system[J]. Journal of Bengbu Medical College, 2016, 41(5):695-698.(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/bbyxyxb201605049 [31] 邹秀琦, 李岚彬, 周东, 等.台风灾害脆弱性评估的分析框架研究[J].福建师范大学学报(自然科学版), 2017, 64(5):76-83. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fjsfdxxb201705011ZOU X Q, LI L B, ZHOU D, et al. A Study on the Analysis Framework of the Assessment of Typhoon Disaster Vulnerability[J]. Journal of Fujian Normal University (Natural Science Edition), 2017, 64(5):76-83.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fjsfdxxb201705011