Analysis of cotton production agglomeration level and influencing factors in China
[Objective]This study aims to understand the changes in the agglomeration level of cotton production in China and its influencing factors.These findings hold practical significance for optimizing the layout of the cotton industry,promoting high-quality development of the cotton industry,and enhancing regional economic development.[Methods]Using the location quotient index to measure the agglomeration level of cotton production in China from 2001 to 2022,comparing the agglomeration growth and competitive status changes of cotton production in major producing provinces using the competitive state model,and finally empirically analyzing the factors affecting agglomeration changes.[Results]From 2001 to 2022,the cotton production aggregation level in inland regions of Northwest China has been consistently higher than that in the Yangtze River basin and the Yellow River basin,and has continued to rise.The production aggregation level and competitiveness of cotton in Xinjiang have been continuously strengthened.However,the production aggregation level and comprehensive competitiveness of cotton in other major cotton-producing provinces have notably declined.Factors affecting the changes in cotton production aggregation in China include conflicts between grain and cotton for land,water resources,the quantity of agricultural labor,agricultural technology levels,transportation costs,the synthetic fiber industry,and cotton target price policy.[Conclusion]For the sustainable development of the cotton industry,it is recommended to scientifically promote the allocation of resources for cotton production,implement the cotton industrial policies in accordance with local conditions,improve the technical level of cotton production,improve the cotton production infrastructure,promote the development of related industries and improve target price subsidy policies.
cottonproduction agglomerationdivisioncompetitive statehigh-quality development
国家科技期刊平台
NSTL
万方数据
