| 基于熵权TOPSIS模型的耕地利用效益评价及障碍因子识别——以东中西三地案例比较为例 |
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| 引用本文： | 马聪,林坚.基于熵权TOPSIS模型的耕地利用效益评价及障碍因子识别——以东中西三地案例比较为例[J].中国农业大学学报,2021,26(8):196-210. |
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| 作者姓名： | 马聪 林坚 |
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| 作者单位： | 北京大学 城市与环境学院, 北京 100871;北京大学 城市与环境学院, 北京 100871; 北京大学 城市规划设计中心, 北京 100871; 北京大学 国土规划与开发国土资源部重点实验室, 北京 100871 |
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| 基金项目： | 国家社会科学基金项目(18VSJ041) |
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| 摘 要： | 为定量化评估不同地区耕地利用效益水平并识别其障碍因子,选取分别处于我国东部、中部、西部的上海市青浦区、长沙市长沙县和固原市彭阳县三地为研究对象,从生态、经济、社会等3个方面构建耕地利用效益评价指标体系,运用熵权TOPSIS模型对不同地区2001—2018年耕地利用效益水平进行评价和比较分析,并采用障碍度模型识别各地区障碍因子。结果表明:1)青浦区生态效益和经济效益总体较高;长沙县生态效益较高,但经济效益低于青浦区;彭阳县生态效益和经济效益均处于较低水平;社会效益方面,各地区总体呈上升趋势。2)经济水平较高和自然条件较好的东部青浦区耕地利用效益水平最高,而经济水平较低和自然条件恶劣的西部彭阳县最低,中部的长沙县则处于中间水平。3)东部青浦区的社会效益是制约其耕地利用效益提升的主要因子,其2016—2018年耕地利用效益的主要障碍指标为人均农业产值、人均粮食产量和土地生产力;长沙县和彭阳县主要影响因子为经济效益,长沙县2016—2018年主要障碍指标为农业机械化效率、劳动生产率等和人均粮食产量,彭阳县则为农业机械化效率、灌溉指数和农民人均纯收入。因此,东中西地区应做到因地制宜,采取针对性措施克服自身主要障碍因子对耕地利用效益水平的影响,从而促进耕地资源合理利用与农业可持续发展。
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| 关 键 词： | 土地利用 农村 效益评价 模型 障碍因子 |
| 收稿时间： | 2020/12/21 0:00:00 |
Evaluation of cultivated land use efficiency based on entropy TOPSIS model and identification of its obstacle factors: A case study of three regions in the east, middle and west of China |
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| MA Cong,LIN Jian.Evaluation of cultivated land use efficiency based on entropy TOPSIS model and identification of its obstacle factors: A case study of three regions in the east, middle and west of China[J].Journal of China Agricultural University,2021,26(8):196-210. |
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| Authors: | MA Cong LIN Jian |
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| Institution: | College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Center of Urban Planning and Design, Peking University, Beijing 100871, China; Key Laboratory of Land Planning and Development, Ministry of Land and Resources, Peking University, Beijing 100871, China |
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| Abstract: | The aim of this study was to evaluate the cultivated land use efficiency in different areas quantitatively and identify their obstacle factors. Qingpu District of Shanghai, Changsha Country of Changsha and Pengyang Country of Guyuan, which are respectively located in the east, middle and west of China, are taken as three study areas. The evaluation index system of cultivated land use efficiency was constructed from 3 dimensions of ecological efficiency, economic efficiency and social efficiency. The entropy TOPSIS method and the obstacle degree model were applied to estimate the cultivated land use efficiency and identify obstacle factors in the three study areas. The results showed that: 1)The ecological and economic benefits in Qingpu District were generally high. The ecological benefits in Changsha County were high, while the economic benefits were lower than that of Qingpu District. Both the ecological and economic benefits in Pengyang County were at a lower level. And the social benefits of three regions increased as a whole. 2)Qingpu, with higher economic level and better natural conditions, had the highest level of cultivated land use efficiency, while Pengyang, with lower economic level and poor natural conditions, had the lowest level. And Changsha was in the middle level. 3)The social efficiency was the major factor restraining the cultivated land use efficiency from improving in Qingpu. Changsha and Pengyang were similar, namely, the economic efficiency was the main obstacle factors. The top three obstacle factors during 2016-2018 in Qingpu were the per capita gross agricultural output value, the per capita grain yield and the land productivity. The obstacale fators in Changsha were the agricultural mechanization efficiency, the labour productivity and the per capita grain yield, while in Pengyang were the agricultural mechanization efficiency, the irrigation rate and the net income per farmer. Therefore, in order to promote the rational utilization of cultivated land resources and the sustainable development of agriculture, three regions should take targeted measures to overcome the impact of their main obstacle factors on the cultivated land use efficiency. |
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| Keywords: | land use rural areas efficiency evaluation models obstacle factor |
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