文章摘要
黑龙江省县域贫困空间格局及其影响因素分析
County level poverty pattern and influencing factors in Heilongjiang Province
投稿时间:2017-10-22  修订日期:2018-02-12
DOI:10.13872/j.1000-0275.2018.0018
中文关键词: 贫困发生率  贫困特征  空间分布  影响因素  县域  黑龙江省
英文关键词: incidence of poverty  poverty characteristics  spatial distribution  influencing factors  county  Heilongjiang Province
基金项目:国家精准扶贫工作成效第三方评估重大任务黑龙江省调查评估课题
作者单位E-mail
杜国明 东北农业大学资源与环境学院 nmgdgm@126.com 
姜莹莹 东北农业大学资源与环境学院 2224967913@qq.com 
孙晓兵 中国农业大学资源与环境学院 xiaobingsun@163.com 
刘文琦 东北农业大学资源与环境学院 1960293809@qq.com 
黎春 东北农业大学资源与环境学院 904782010@qq.com 
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中文摘要:
      科学准确地刻画贫困空间格局并揭示其影响机制是制定区域扶贫政策的重要依据。以县域为单元,在对黑龙江省贫困空间格局进行分析的基础上,运用多元回归模型量化贫困程度的影响因素,揭示县域贫困的影响机制。结果表明:黑龙江省县域贫困发生率均值为3.92%,贫困县和非贫困县的贫困发生率均值分别为8.98%和1.19%,县域贫困发生率高值区主要分布于干旱盐碱区、漫川漫岗区、低山丘陵区和偏远农业区。黑龙江省县域贫困发生率影响因素包括县域城镇化率、人均耕地面积、平均温度、坡度、水田比率、耕地质量等别、第一产业产值比重、垦殖率和中小学在校生比例,其中,前6 项指标呈负相关,后3 项呈正相关。社会经济、和土地利用等经济因素对县域贫困发生率的影响具有直接性与短期性,而气候和地貌等自然地理状况对县域贫困发生率的影响具有间接性与长期性。因此,黑龙江省应结合气候特征和地形地貌状况,充分挖掘贫困县社会经济与土地利用发展潜力,因地制宜地制定扶贫措施,确保扶贫工作能够切实有效。
英文摘要:
      It is necessary to study spatial pattern of poverty at county level and to identify its influence factors for the formulation of regional poverty alleviation policy. This paper applied a multiple regression model to quantify the influencing factors of poverty degree and analyzed the impact mechanism in Heilongjiang Province. The results showed that the average incidence of poverty in the counties of Heilongjiang Province was 3.92% in 2016. The mean rates of poverty and poor poverty were 8.98% and 1.19% for poor and non-poor counties respectively. Areas of high incidence of poverty in Heilongjiang Province are mainly in arid saline-alkali, areas alluvial flooding plain, low hilly and remote agricultural areas. Impacting factors for the incidence of poverty in Heilongjiang Province include the urbanization rate, per capita arable land, average temperature, slope, paddy field ratio, cultivated land quality and percentage of primary industry, reclamation rate and the ratio of primary and secondary school students. Urbanization rate, per capita arable land, average temperature, slope, paddy field ratio and cultivated land quality were negatively correlated with the incidence of poverty. The percentage of primary industry, the reclamation rate and the ratio of primary and secondary school students showed a positive correlation. While economic factors such as social economy and land use had direct and short-term effects on the incidence of poverty, natural conditions (e.g., climate and geomorphology) have long-term and indirect impacts on the county poverty incidence. Consequently, climatic characteristics and topography should be considered in planning socio-economy and land-use development in poverty-stricken counties in Heilongjiang Province. Similarly, local conditions should be taken into accounting when formulating poverty alleviation measures for better implementation.
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