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李兆广, 陈转青, 张景博. 基于熵值TOPSIS法的农业绿色发展时空水平测度研究[J]. 西部经济管理论坛, 2023, 34(5): 59-70. DOI: 10.12181/jjgl.2023.05.06
引用本文: 李兆广, 陈转青, 张景博. 基于熵值TOPSIS法的农业绿色发展时空水平测度研究[J]. 西部经济管理论坛, 2023, 34(5): 59-70. DOI: 10.12181/jjgl.2023.05.06
Li Zhaoguang, Chen Zhuanqing, Zhang Jingbo. Spatial and temporal level measurement of agricultural green development based on entropy TOPSIS method[J]. West Forum on Economy and Management, 2023, 34(5): 59-70. DOI: 10.12181/jjgl.2023.05.06
Citation: Li Zhaoguang, Chen Zhuanqing, Zhang Jingbo. Spatial and temporal level measurement of agricultural green development based on entropy TOPSIS method[J]. West Forum on Economy and Management, 2023, 34(5): 59-70. DOI: 10.12181/jjgl.2023.05.06

基于熵值TOPSIS法的农业绿色发展时空水平测度研究

Spatial and temporal level measurement of agricultural green development based on entropy TOPSIS method

  • 摘要: 农业绿色发展是保障国家粮食安全和促进农村现代化的必要手段,构建合适的评价指标体系并对不同地区进行时空水平测度有利于推动我国农业绿色发展。本文通过构建绿色生产环境、农业生产投入、绿色生产方式和农业绿色产出四个维度包含19个指标的农业绿色发展评价体系,收集2012—2021年各省份的相关统计数据,利用熵值TOPSIS法,对全国及各省农业绿色发展水平进行测度。结果表明:(1)近年来我国农业绿色发展水平呈上升趋势,绿色生产环境与绿色生产方式得分较高,而农业生产投入与农业绿色产出增速较快;(2)不同地区农业绿色发展水平以及各二级指标得分仍有较大差距,其中内蒙古、黑龙江以及宁夏农业绿色发展水平位于我国前列;(3)运用聚类分析,将选取的31个省份分为绿色高效型、绿色提升型、资源依赖型、绿色发展型和产出低效型五类,相同类别省份呈现区域聚集特征。为加快促进我国农业绿色发展,应采取加快新型农业经营主体培育、构建跨产业生态补偿机制和推进实用型农业生产技术创新等措施。

     

    Abstract: Agricultural green development is a necessary means to ensure national food security and promote rural modernization. Constructing a suitable evaluation indicator system and conducting spatiotemporal assessments in different regions can facilitate the advancement of agricultural green development in China. This paper establishes an evaluation system for agricultural green development, consisting of four dimensions: green production environment, agricultural production input, green production methods, and agricultural green output, with a total of 21 indicators. Data from various provinces for the years 2012 to 2021 were collected, and the entropy value-TOPSIS method was used to measure the level of agricultural green development at the national and provincial levels. The results indicate that: (1) In recent years, China’s agricultural green development has been on the rise, with higher scores for the green production environment and green production methods, while agricultural production input and the growth rate of agricultural green output are also increasing; (2) There are still significant disparities in the levels of agricultural green development among different regions and in the scores of various secondary indicators. Inner Mongolia, Heilongjiang, and Ningxia are among the leading provinces in agricultural green development; (3) Through cluster analysis, the 31 provinces are categorized into five types: green and efficient, green improvement, resource-dependent, green development, and low-efficiency output. Provinces within the same category exhibit regional clustering characteristics. To accelerate the promotion of agricultural green development in China, measures such as fostering the cultivation of new agricultural entities, establishing cross-industry ecological compensation mechanisms, and advancing practical agricultural production technology innovation should be implemented.

     

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