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作 者:杜旭[1] 宋戈[1,2] 李瑞雪[1] 曲炳佳 杨怡然[1]
机构地区:[1]东北农业大学资源与环境学院,哈尔滨150030 [2]东北大学土地资源管理研究所,沈阳110819
出 处:《水土保持研究》2016年第1期139-144,共6页Research of Soil and Water Conservation
基 金:国家社会科学基金"资源型城市土地集约利用的对策研究"(07CJY025)
摘 要:在我国人地矛盾日益突出的背景下,节约集约利用土地已成为社会发展的必然选择,同时也成为促进我国城市健康和可持续发展的关键。以黑龙江省煤炭型城市——鸡西市、双鸭山市、七台河市、鹤岗市为研究区,从经济潜力、社会潜力、生态潜力3方面构建研究区土地集约利用评价指标体系,采用层次分析法与熵值法确定权重,运用多因素综合评价法对研究区2004—2013年的土地集约利用进行综合评价,并分析其驱动力因素。研究结果表明:2004—2006年,黑龙江省煤炭型城市处于不集约状态,属于土地集约利用等级层次中的Ⅳ级,2007—2013年,黑龙江省煤炭型城市处于集约状态,属于土地集约利用等级层次中的Ⅲ级;地均固定资产投资、地均GDP、煤炭开采机械化率、煤炭产值、城市人口与用地增长弹性系数、煤炭挖掘面平均月进度指标是影响煤炭城市土地集约利用的驱动因子。Under the background of the increasingly prominent contradiction between human and land in China,economical and intensive use of land has become the inevitable choice of social development,at the same time,it also becomes the key to promoting the healthy and sustainable development city in China.Taking coal-mining cities in Heilongjiang Province,Jixi,Shuangyashan,Qitaihe,Hegang as research areas,we put forward the evaluation index system of intensive land utilization in the study areas from aspects of economic potential,social potential and ecological potential.We used the analytic hierarchy process and entropy value method to determine the weight,used the multi-factor comprehensive evaluation method to make a comprehensive evaluation on intensive land use of the study areas during the period from 2004to2013,and analyze the driving factors.The results showed that coal-mining cities in Heilongjiang Province were not in the intensive state,belonging to the intensive land utilizationⅣlevel in the hierarchy during the period from 2004 to 2006,during the period from 2007 to 2013,coal-mining cities in Heilongjiang Province were in the intensive state,belonging to the land intensive utilization Ⅲlevel in the hierarchy;the effective driving factors on intensive land utilization of coal-mining cities were fixed asset investment,GDP per area,the mechanization rate of coal mining,coal output,urban population and land growth elasticity coefficient,and coal mining face average monthly progress index.
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