产业生态化与生态产业化耦合协调时空演变及驱动因素研究——以福建省为例
Study on the Spatio-Temporal Evolution and Driving Factors of the Coupling Coordination between Industrial Ecology and Ecological Industrialization——Take Fujian Province as an Example
陈静 1付华鑫1
作者信息
摘要
产业生态化与生态产业化协同发展是在供给侧结构性改革下实现经济高质量发展的重要途径.基于2011年-2021年福建省9市相关数据,利用耦合协调模型、莫兰指数法及灰色关联分析法对"两化"耦合协调发展程度、时空演变特征及驱动因素进行实证研究.研究发现:福建省9市产业生态化、生态产业化及"两化"耦合协调水平均总体呈上升的趋势,但各市域之间具有差异性;从空间自相关来看,福建省"两化"耦合协调水平整体呈空间负相关,且呈现阶段性空间集聚效应;在耦合协调度的驱动因素中,人均GDP、第三产业占GDP比重、人力资本水平、信息化水平和节能环保支出的灰色关联度排在前五位.根据结论得出福建省在"两化"耦合协调发展方向上的启示.
Abstract
The coordinated development of industrial ecology and ecological industrialization is an im-portant way to achieve high-quality economic development under supply-side structural reform.Based on relevant data of nine cities in Fujian Province from 2011 to 2021,an empirical study is carried out on the degree of coupled coordinated development,spatio-temporal evolution characteristics and driving factors of"Two Modernizations"using the coupled coordination model,Moran index method and gray correlation analysis method.Research found:The level of industrial ecology,ecological industrialization and the coupling coordination of"Two Modernizations"in the nine cities of Fujian Province are general-ly on the rise,but there are differences among cities;From the perspective of spatial autocorrelation,the overall level of"Two Modernizations"in Fujian Province is negatively spatially correlated and pres-ents a phased spatial agglomeration effect;Among the driving factors of coupling coordination,per cap-ita GDP,tertiary industry's proportion in GDP,human capital level,informatization level and gray corre-lation of energy saving and environmental protection expenditures rank among the top five.Based on the conclusions,the enlightenment of Fujian Province on the coupled and coordinated development di-rection of"Two Modernizations"is drawn.
关键词
产业生态化/生态产业化/耦合协调/灰色关联度Key words
Industrial Ecology/Ecological Industrialization/Coupling Coordination/Gray Correlation Degree引用本文复制引用
出版年
2024
NETL
NSTL
万方数据