滇池流域农业绿色发展水平综合评价及影响因素研究

Study on Comprehensive Evaluation and Influencing Factors of Agricultural Green Development Level in Dianchi Lake Basin

  • 摘要: 推动滇池流域农业绿色高质量发展,对促进滇池治理和生态环境保护具有重要的现实意义。本文以滇池流域的7个县区为研究对象,从资源节约、环境友好和高效发展三个层面,结合滇池流域农业绿色发展的区域性特征,构建了15个适合滇池流域农业绿色发展的评价指标体系,运用熵值法对滇池流域农业绿色发展水平进行综合评价,运用灰色关联分析法对滇池流域农业绿色发展影响因素进行排序。结果表明:(1)2015—2020年,滇池流域农业绿色发展水平得到提升,但发展并不稳定和不平衡;(2)环境友好发展水平相对较高,但是资源节约和高效发展依旧是短板,同时高效发展增速最慢;(3)影响滇池流域农业绿色发展水平最大的因素是环境友好,高效发展影响适中,资源节约影响最小;(4)具体指标中,影响最大的因素是农业服务业贡献率、农村人均可支配收入、农药施用强度、湖泊营养状态指数和森林覆盖率。

     

    Abstract: It is of great practical significance to promote the green and high-quality development of agriculture in Dianchi Lake Basin to promote the management and ecological environment protection of Dianchi Lake.This paper took seven counties in the Dianchi Lake Basin as the research object, constructed 15 evaluation index systems suitable for the agricultural green development of the Dianchi Lake Basin from three levels of resource conservation, environmental friendliness and efficient development, and combined with the regional characteristics of agricultural green development in Dianchi Lake Basin, and used the entropy method to comprehensively evaluate the agricultural green development level of the Dianchi Lake Basin. Grey relational analysis was used to rank the influencing factors of agricultural green development in Dianchi Lake Basin.The results showed that, (1) From 2015 to 2020, the level of agricultural green development in Dianchi Basin had been improved, but the development was not stable and unbalanced; (2) The level of environmentally friendly development was relatively high, but resource conservation and efficient development were still the weaknesses, and efficient development had the slowest growth rate; (3) The biggest factors affecting the level of agricultural green development in Dianchi Lake Basin were environmental friendliness, efficient development had moderate impact, and resource conservation had minimal impact; (4) Among the specific indicators, the most influential factors were the contribution rate of agricultural service industry, rural per capita disposable income, pesticide application intensity, lake nutrient status index and forest coverage rate.

     

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