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Study On The Measurement And Promotion Path Of Agricultural Green Development Level In China

Posted on:2020-04-01Degree:MasterType:Thesis
Country:ChinaCandidate:X YangFull Text:PDF
GTID:2393330575475808Subject:Applied statistics
Abstract/Summary:PDF Full Text Request
In 2017,China proposed to put the green development of agriculture in a prominent position in the overall construction of ecological civilization,and establish a comprehensive system oriented by green ecology.Agricultural green development is related to the ecological environment,food security,farmers’ income and other major issues,but at present,China’s agriculture is facing serious non-point source pollution,high intensity of resource utilization,poor quality of agricultural products.Therefore,it is of great theoretical and practical significance for the government to explore the current situation of agricultural green development in China,to find regional development differences,and to plan the promotion path pertinently,so as to formulate relevant policies and improve the quality of agricultural products.The main work of this study is as follows:(1)Constructing the index system of agricultural green development.Firstly,according to the general situation of agricultural green development in China,referring to the "Green Development Index System" issued by the Development and Reform Commission,the primary evaluation index system of agricultural green development in this paper is formulated from the five aspects of resource utilization,origin environment,ecological environment,green supply and economic benefits;then the index system is improved through qualitative and quantitative tests;finally,the index system is improved.Structural optimization is carried out to ensure the integrity and correctness of the index system.(2)The weight is determined by the combination of entropy method and BP neural network weighting method.Based on the evaluation index system of agricultural green development established in this paper,the expected output value is determined by using the method of entropy value,and then the output value is simulated by BP neural network,the weights are calculated,and the comprehensive scores and rankings of provinces are calculated.It is concluded that there are regional differences in the green development of agriculture in China at the present stage.Thelevel of green development in the northeast and southeast coastal areas of China is more prominent.The green development of agriculture in the major traditional agricultural provinces and central areas tends to be general.The green development of agriculture in the economically underdeveloped northwest areas is still poor.(3)Introducing the sub-constraint theory to determine the advantages and disadvantages of influencing the green development level of agriculture.Firstly,the ranking of provincial optimal solutions under the condition of no secondary constraint is calculated;secondly,the ranking of provincial optimal solutions under the condition of no secondary constraint is calculated by taking five criteria levels as secondary constraint conditions;finally,the ranking of provincial optimal solutions under the condition of no secondary constraint is compared with the ranking under the condition of no secondary constraint.If the ranking of provincial optimal solutions with secondary constraint is higher,the secondary constraint is the dominant factor of provincial agricultural green development,otherwise,It is the disadvantage factor.(4)Based on the research results of the measurement of agricultural green development level,the promotion path is put forward.According to the current situation of green development of agriculture in China and the existing national policies,combined with the research results,the paper puts forward some reference suggestions for sustainable development of agriculture in China,and provides suggestions for further changing the mode of agricultural production and improving the quality of agricultural products.
Keywords/Search Tags:agricultural green development, BP neural network, entropy method, sub-constraint model lifting path
PDF Full Text Request
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