| 基于Bayes判别的工业用地集约利用评价与潜力挖掘分析——以湖北省典型企业为例 |
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| 引用本文： | 陈昱,陈银蓉,马文博.基于Bayes判别的工业用地集约利用评价与潜力挖掘分析——以湖北省典型企业为例[J].资源科学,2012,34(3):433-441. |
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| 作者姓名： | 陈昱 陈银蓉 马文博 |
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| 作者单位： | 1. 华中农业大学土地管理学院,武汉430070／湖北农村发展研究中心,武汉430070
2. 西北农林科技大学经济管理学院,杨陵,712100 |
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| 基金项目： | 国家自然科学基金(编号：70473029);国家社会科学基金(编号：09BZZ024);中国水电工程顾问集团公司科技项目(编号：CHC-KJ-2007-21-4)。 |
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| 摘 要： | 工业用地作为建设用地的主要类型和城市土地的重要组成部分,其集约化程度直接影响到城市土地利用状况。在512份调查问卷的基础上,以食品制造企业为例,构建了由用地结构、用地强度、土地投入和土地产出4个子目标层、9个因素层组成的工业企业土地集约利用评价指标体系。以指标现状值为基础,采用区间估计方法来确定指标理想值,并构建Bayes判别函数实现了对湖北省52家典型食品制造企业的土地集约利用度判别。结果显示：仅有13.46%的企业土地为集约利用,低度和中度利用企业占样本总数的67.31%,样本企业土地集约利用可挖掘潜力约为100.48hm2。Bayes判别由于对样本信息利用充分、全面,因而判别准确性较高,采用该方法对工业企业土地集约利用度实施评价,为土地集约利用问题的研究提供了一种新的思路。
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| 关 键 词： | 食品制造企业 集约利用 潜力挖掘 区间估计 Bayes判别 |
Evaluation of Industrial Land's Intensive Use and Analysis of Potential Mining with Bayes Discrimination: A Case Study of Typical Enterprises in Hubei Province |
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| CHEN Yu,CHEN Yinrong and MAWenbo.Evaluation of Industrial Land''s Intensive Use and Analysis of Potential Mining with Bayes Discrimination: A Case Study of Typical Enterprises in Hubei Province[J].Resources Science,2012,34(3):433-441. |
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| Authors: | CHEN Yu CHEN Yinrong and MAWenbo |
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| Institution: | Collge of Land Management, Huazhong Agricultural University, Wuhan 430070, China;Hubei Rural Development Research Center,Wuhan 430070, China;Collge of Land Management, Huazhong Agricultural University, Wuhan 430070, China;Hubei Rural Development Research Center,Wuhan 430070, China;College of Economics and Management, Northwest Agriculture & Forestry University, Yangling 712100, China |
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| Abstract: | Industrial land is a major kind of construction land as well as an important component of urban land, and its intensive degree directly influences the use of urban land. This paper takes Hubei Province as an example, selects the enterprises with better production status and comparatively higher profits or tax within the same industry to investigate. Based on 512 questionnaires and with food manufacturing industry as an example, the paper has constructed an intensive land use evaluation system for industrial land, which is composed of 4 objective layers (land use construction, land use intensity, land investment and land productivity) and 9 element layers. With indicators'status value and the actual economic development of Hubei, the authors have used interval estimation method to determine the ideal value of each indicator and constructed Bayes discrimination function to discriminate the land use intensity of 52 typical food manufacturing companies. Besides, after evaluating the potentiality of intensive land use for enterprises which have used the land at a moderate or low level with geometric mean algorithm, we got the results as follows: among 52 enterprises, 10 are evaluated to use land excessively, which account for 19.23% of total samples; 7 are evaluated to use land intensively, which account for 13.46% of total samples; 3 are evaluated to use land moderately, which account for 5.77% of total samples; 32 are evaluated to use land at a low level, which account for 61.54% of total samples. In addition, there are approximately 100.48 hm2 potential lands for the 52 food manufacturing enterprises, accounting for 55.83% of current land, which shows great potentiality for intensive use. In order to improve the intensive degree of land, the following suggestions were recommended in this paper: 1)For enterprises that overused the land, investment of technological innovation should be increased and reliance on land should be reduced; 2)For enterprises that used the land at a moderate or low level, supervision on land use should be strengthened and land with insufficient capital investment or with no use should be transferred or redistributed; 3)Strictly obey relevant rules on land use approval and comply with land use standards of different industries to control land supply, improve market mechanism of industrial land supply and bring into play the price's fundamental effect on land resources distributions. |
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| Keywords: | Food manufacturing enterprise Intensive use potential mining Interval estimation Bayes discrimination |
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