摘要
基于多源遥感数据提取投入产出数据,采用考虑非期望产出的超效率EBM模型对2000—2015年山东省县域生态效率进行测度,在此基础上采用核密度估计、空间自相关等方法对山东省县域生态效率的时空特征进行分析。研究表明:①山东省县域生态效率呈现波动式发展趋势;高值区与低值区存在显著空间分化,胶东半岛与济南都市圈构成高值集聚区,鲁西北、鲁西南、鲁南地区形成低值连绵带;②山东省县域生态效率无明显的两极分化现象,处于高值区、低值区的县域生态效率值变化较大,生态效率空间非均衡性逐渐扩大;③山东省县域生态效率存在显著空间正相关,且空间集聚性呈现增强态势;县域生态效率存在空间俱乐部趋同特征。
Input-output data are extracted based on multi-source remote sensing data,we used undesirable-output super efficiency EBM(Epsilon-based Measure)model to measure eco-efficiency of the country from 2000 to 2015 in Shandong Province.On this basis,we used kernel density estimation,spatial autocorrelation method to analyze spatial-temporal characteristics of county eco-efficiency in Shandong Province.The results shows:(1)Eco-efficiency of the country shows a fluctuating development trend in Shandong Province;There is obvious spatial disequilibrium in eco-efficiency of high value area and low value area,Jiaodong peninsula and Jinan metropolitan area constitute high-value cluster area,and low-value continuous zone is formed in the northwest,southwest and south of Shandong Province.(2)There is no obvious polarization in county eco-efficiency in Shandong Province,eco-efficiency of the county in high value areas and low value areas vary greatly and spatial agglomeration has an increasing tendency,spatial disequilibrium of eco-efficiency is gradually expanding.(3)There is an obvious positive spatial correlation in county of eco-efficiency in Shandong Province;Eco-efficiency of the country has characteristics of spatial club convergence.
作者
李宵宵
赵林
刘焱序
蒋正举
蔡利平
LI Xiaoxiao;ZHAO Lin;LIU Yanxu;JIANG Zhengju;CAI Liping(College of Geography and Tourism,Rizhao 276826,China;College of Management,Qufu Normal University,Rizhao 276826,China;Faculty of Geography Science,Beijing Normal University,Beijing 100875,China)
出处
《世界地理研究》
CSSCI
北大核心
2022年第1期120-129,共10页
World Regional Studies
基金
国家自然科学基金项目(41701117)
山东省自然科学基金项目(ZR2019PG005)
山东省高等学校人文社会科学研究项目(J16YE09)。
关键词
生态效率
县域单元
超效率EBM模型
多源遥感数据
山东省
eco-efficiency
county unit
Super-EBM model
multi-source remote sensing data
Shandong Province