山东省县域生态效率测算与时空特征分析

doi:10.3969/j.issn.1004-9479.2022.01.2020165

摘要/Abstract

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。

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

Abstract:

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.

Key words: eco-efficiency, county unit, Super-EBM model, multi-source remote sensing data, Shandong Province

李宵宵, 赵林, 刘焱序, 蒋正举, 蔡利平. 山东省县域生态效率测算与时空特征分析[J]. 世界地理研究, 2022, 31(1): 120-129.

Xiaoxiao LI, Lin ZHAO, Yanxu LIU, Zhengju JIANG, Liping CAI. Eco-efficiency measurement and spatial-temporal characteristics analysis of county-scale in Shandong Province[J]. World Regional Studies, 2022, 31(1): 120-129.

图/表 6

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指标	类别	具体指标构成	说明
投入指标	资源消耗	能源消耗	DMSP/OLS夜间灯光数据
		土地资源消耗	建设用地面积、耕地面积
		劳动力资源消耗	年末人口总量
产出指标	经济产出	经济发展总量	地区生产总值
产出指标	环境产出	环境污染物	PM_2.5年均浓度

指标	类别	具体指标构成	说明
投入指标	资源消耗	能源消耗	DMSP/OLS夜间灯光数据
		土地资源消耗	建设用地面积、耕地面积
		劳动力资源消耗	年末人口总量
产出指标	经济产出	经济发展总量	地区生产总值
产出指标	环境产出	环境污染物	PM_2.5年均浓度

指标	拟合优度R²
指标	线性相关	指数相关	对数相关
耕地	0.9845	0.9816	0.9799
建设用地	0.9300	0.9118	0.9185
人口	0.9627	0.9672	0.9563
地区GDP	0.8395	0.6805	0.8748
夜间灯光总量与能源消耗总量	0.9674	0.8424	0.9896
PM_2.5总量与废气排放量	0.7020	0.8539	0.684

指标	拟合优度R²
指标	线性相关	指数相关	对数相关
耕地	0.9845	0.9816	0.9799
建设用地	0.9300	0.9118	0.9185
人口	0.9627	0.9672	0.9563
地区GDP	0.8395	0.6805	0.8748
夜间灯光总量与能源消耗总量	0.9674	0.8424	0.9896
PM_2.5总量与废气排放量	0.7020	0.8539	0.684

参数	年份
参数	2000年	2005年	2010年	2015年
Moran’s I值	0.1079	0.1619	0.4231	0.3796
Z值	2.3991	2.4396	6.1627	5.5139
P值	0.0164	0.0147	0.001	0.001