江苏省县域森林生态安全评价及空间计量分析

County forest ecological security evaluation and spatial econometric analysis in Jiangsu Province

通过考察生态区位因素对森林生态安全的影响,建立评价指标体系,研究其空间相关性的内在效应机制,从而实现森林生态安全的评价与监测。以江苏省80个区县为研究对象,基于2000—2015年面板数据,运用熵权法、专家法及模糊物元法计算森林生态安全指数,然后结合气象类指标及地形类指标计算生态区位系数,再用该系数修正森林ESI,同时结合Arc GIS技术、空间相关性分析、SLM与SEM模型得出如下结论:(1)人口密度、单位面积能源消耗量、退耕还林面积占比等指标权重最大;(2)生态区位系数高值区主要分布在江苏南部少数地区,低值区主要分布在江苏东北部;(3)苏南地区森林生态安全状况整体好于苏北及中东部地区;(4) 2000—2015年,江苏省67.5%的区县森林ESI呈现出较明显下降趋势,反映出江苏省森林生态安全发展状况不容乐观;(5)江苏省县域森林ESI整体空间相关性显著(P≤0.01),但2000—2015年空间聚集程度有所下降,且Low-Low聚类显著性更强;(6)森林ESI在江苏省县域间为扩散效应与回流效应并存。

The aim of the present study was to examine the impact of ecological location factors on forest ecological security,establish an evaluation index system and study the internal effect mechanism of its spatial correlation to evaluate and monitor forest ecological security.Based on 80 counties in Jiangsu Province forest panel data from 2000 to 2015 were analyzed in this study using the entropy,expert,and fuzzy matter element methods to calculate the forest ecological security index,which was adjusted according to the ecological location model obtained from the meteorological and terrain indexes.Moreover,the combined use of Arc GIS software,ESDA and CSDA models in the present study revealed that( 1) population density,unit area energy consumption and proportion of reforestation area are the most important measures;( 2) the area with the highest ecological location coefficient was mainly distributed in southern Jiangsu Province. Further,the area with the lowest ecological location coefficient was mainly distributed in the northeast;( 3) the forest ecological security status in southern Jiangsu was better than that in northern and mideastern Jiangsu;( 4) regarding forest cover change from 2000 to 2015,65%of the ESI index of all counties in Jiangsu Province showed an obvious declining trend;( 5) the overall spatial correlation ofthe forest ESI index in counties of the Jiangsu Province was significant,but the spatial aggregation in 2000-2015 decreased and Low-Low clustering was more significant( P≤0.01);and( 6) the diffusion and reflux effects between the forest ESI index in the counties of Jiangsu Province were concomitant.