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Study On Transformation Efficiency And Influencing Factors Of Resource-based Cities In Anhui Province

Posted on:2023-09-19Degree:MasterType:Thesis
Country:ChinaCandidate:W X JiFull Text:PDF
GTID:2569306764492744Subject:Engineering
Abstract/Summary:
The resources of some resource-based cities in Anhui province have been exhausted or almost exhausted.The economic and social development of cities have been restricted.The impetus of urban transformation is insufficient.The ecological environment has been damaged.As a consequence,in order to get rid of the "resource curse" of resource-based cities in Anhui Province,it is necessary to fully study the allocation of resource elements,improve the efficiency of urban transformation and development,and promote the green and sustainable development of resource-based cities in Anhui Province better and faster.This paper systematically analyzed the development status of nine resource-based cities in Anhui province,namely Huaibei,Bozhou,Suzhou,Huainan,Chuzhou,Ma ’anshan,Xuancheng,Tongling and Chizhou,from four aspects of industrial development,social development,scientific and technological innovation and ecological environment.The index system of urban transformation efficiency was constructed from five aspects: resource consumption,social input,economic level,social livelihood and ecological environment.The radial super efficiency DEA model was used to reckon their transformation efficiency of nine resource-dependent cities in Anhui Province from 2010 to 2020.The transformation efficiency was dynamically calculated by applying Malmquist index model.Nine cities were divided into two regions and three resource types,and comparative studies were conducted according to regional differences and resource types.After calculating and obtaining the value of urban transformation efficiency,in order to lucubrate its influencing factors,the influencing factors of transformation efficiency were analyzed by panel regression with urban transformation efficiency as the explanatory variable and seven indicators of industrial structure,foreign economy,resource dependence,government support ability,education level,innovation ability and ecological environment as the explained variables.In accordance with the empirical analysis results,feasible countermeasures and suggestions were put forward.The results showed that:(1)The overall transformation efficiency of resource-based cities in Anhui province has increased,but there are great differences in transformation efficiency.Ma’anshan has the best transformation efficiency,and Chizhou ranks lower.(2)The transformation efficiency of resource-based cities in southern Anhui was higher than that in northern Anhui.The transformation efficiency of metal ore cities and non-metal ore cities showed a W-shaped change,while the transformation efficiency of coal cities fluctuates little,and the overall transformation efficiency increased slowly,and there was room for improvement.(3)Among the influencing factors,the upgrading of industrial structure,the expansion of opening and improvement of education level could improve the transformation efficiency.The efficiency of government financial expenditure and S&T innovation output were insufficient.Coal cities highly depended on resources,so that extractive and manufacturing industries had a significant inhibiting effect on urban transformation efficiency.Through empirical analysis,this thesis offered feasible proposals for the transformation of resource-based cities in Anhui Province.First,they should develop emerging industries and readjust the industrial structure.Second,they need to improve infrastructure and improve people’s livelihood.Thirdly,they should establish a talent cultivation mechanism and improve the ability of S&T innovation.Fourth,they should develop and use green energy sources and improve environmental quality.Figure [11] Table [18] Reference [70]...
Keywords/Search Tags:Resource-based cities, Anhui Province, Transformation efficiency, Influencing factors, Super efficiency DEA model, Malmquist index, Panel regression model
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