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Research On Comprehensive Competitiveness Evaluation Of Characteristic Towns In Zhejiang Province

Posted on:2020-11-23Degree:MasterType:Thesis
Country:ChinaCandidate:Z M WuFull Text:PDF
GTID:2417330572961512Subject:Statistics
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In January 2016,Li Qiang,then Governor of Zhejiang Province,published "The Characteristic Town is a Strategic Choice for Innovation and Development in Zhejiang Province",summarizing that under the new economic normal,Zhejiang Province needs to use its own economic,cultural and natural advantages to create small features.The town solves the problem of small development space in Zhejiang Province,insufficient supply,insufficient aggregation of high-end factors,and a deteriorating environment As the earliest province to build characteristic towns,Zhejiang Province has been committed to the urbanization construction and the transformation and upgrading of the economic structure.As a new type of small towns and new economic regions,Zhejiang Province is a new normal economy and urban and rural development.Urgent needs.With the cultivation of the first,second and third batches of characteristic towns in Zhejiang Province,the bottlenecks that emerged during the development of characteristic towns are followed.How to develop quickly and achieve the goal of building characteristic towns needs to be formulated The comprehensive competitiveness evaluation system of characteristic towns will guide the development and planning of the town through scientific and accurate data analysis,and then comprehensively improve the comprehensive competitiveness level of the characteristic towns in Zhejiang Province.This paper studies and analyzes the characteristics,development characteristics and comprehensive competitiveness of characteristic towns through literature analysis.Combining the development status,development status and experience summary of domestic and foreign characteristic towns,through offline research,Zhejiang An in-depth analysis of the development of provincial towns.Based on the urban comprehensive competitiveness model of well-known scholars at home and abroad,starting from the connotation of characteristic towns and the composition and characteristics of comprehensive competitiveness,the theoretical model of comprehensive competitiveness of characteristic towns is constructed.According to the theoretical model,combined with the actual development of Zhejiang's characteristic small towns,from the three dimensions of core competitiveness,internal competitiveness and external competitiveness,human resources,capital resources,government services,environmental resources,infrastructure,industrial scale,technological innovation Seven secondary indicators and 46 third-level indicators are used to construct an evaluation index system for comprehensive competitiveness of characteristic towns in Zhejiang Province.According to the data of 73 characteristic towns in Zhejiang Province in 2016,the Delphi method and the analytic hierarchy process are used to evaluate the comprehensive competitiveness of various characteristic towns in Zhejiang Province,and the comprehensive competitiveness of Zhejiang special towns is graded by xgboost model.The BP neural network model is used to simulate and simulate the comprehensive competitiveness evaluation system of characteristic towns in Zhejiang Province.Finally,based on the analysis of empirical results,provide advice and suggestions for government decision-making departments.Through a large amount of literature reading,theoretical research,field research,and empirical research on the comprehensive competitiveness of Zhejiang's characteristic towns,the main conclusions are as follows:First,foreign famous towns rely on their own environment location,resources and other advantages,and Through the government's adjustment of preferential policies such as taxation,talents,and enterprises,the company will continue to develop its advantageous industries and related industries,form a complete characteristic industrial cluster,and comprehensively improve the comprehensive competitiveness of the town.Second,the overall comprehensive competitiveness of Zhejiang's characteristic towns is not high,and the comprehensive competitiveness of towns with different characteristics in different regions and industries is uneven.The comprehensive competitiveness of Hangzhou's characteristic towns is good,and the competition between Taizhou and Jinhua's characteristic towns is comprehensive.The level of power is low,the level of comprehensive competitiveness of the fashion industry and the information economy industry is high.The comprehensive competitiveness of the characteristic towns of environmental protection and health industry is relatively poor,and the comprehensive competitiveness level is low.Clear,lack of innovation ability,and the industry foundation is not solid.Thirdly,the xgboost model is used to predict the level of characteristic towns.The results of the classification model show the output value of characteristic industries,the proportion of characteristic industrial enterprises,the concentration of characteristic industries,the layout of government industry,the number of business incubators and mass creation space,and the R&D expenditure per 10,000 yuan.Indicators such as the amount of money are important factors in the classification of characteristic towns.Government services,infrastructure,industry scale and technological innovation are four aspects that need to be strengthened in a small town with weak comprehensive competitiveness.Fourthly,the BP neural network model is used to predict the comprehensive competitiveness score of the characteristic towns.The training error is 0.3379,and the test error is 1.3251.The simulation results in the densely distributed areas of the characteristic small towns are excellent,and the overall fitting effect of the model is good.Better,the forecast is more accurate,and can be used as a comprehensive competitiveness evaluation model for characteristic towns.
Keywords/Search Tags:characteristic town, comprehensive competitiveness, evaluation system, xgboost model, BP neural network
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