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Spatial and Temporal Evaluation of Ecological Footprint Intensity of Jiangsu Province at the County-Level Scale
International Journal of Environmental Research and Public Health Pub Date : 2020-10-26 , DOI: 10.3390/ijerph17217833
Decun Wu , Jinping Liu

Due to the high ecological pressure that exists in the process of rapid economic development in Jiangsu Province, it is necessary to evaluate its ecological footprint intensity (EFI). This article focuses on ecological footprint intensity analysis at the county scale. We used county-level data to evaluate the spatial distributions and temporal trends of the ecological footprint intensity in Jiangsu’s counties from 1995 to 2015. The temporal trends of counties are divided into five types: linear declining type, N-shape type, inverted-N type, U-shape type and inverted-U shape type. It was discovered that the proportions of the carbon footprint intensity were maintained or increased in most counties. Exploratory spatial data analysis shows that there was a certain regularity of the EFI spatial distributions, i.e., a gradient decrease from north to south, and there was a decline in the spatial heterogeneity of EFI in Jiangsu’s counties over time. The global Moran’s index (Moran’s I) and local spatial association index (LISA) are used to analyze both the global and local spatial correlation of EFIs among counties of Jiangsu Province. The high-high and low-low agglomeration effects were the most common, and there were assimilation impacts of counties with strong agglomeration on adjacent units over time. The results implied the utility of differentiated EFI reduction control measures and promotion of low-low agglomeration and suppression of high-high agglomeration in EFI-related ecology policy.

中文翻译:

县域尺度上江苏省生态足迹强度时空评价

由于江苏省经济快速发展过程中存在很高的生态压力,因此有必要对其生态足迹强度(EFI)进行评估。本文着重于县域范围内的生态足迹强度分析。我们使用县级数据评估了1995年至2015年江苏各县的生态足迹强度的空间分布和时间趋势。县的时间趋势分为五种类型: 线性下降型,N型,倒N型,U型和U型。据发现,在大多数县,碳足迹强度的比例得以保持或增加。探索性空间数据分析表明,江苏省各县市EFI空间分布有一定规律性,即从北向南梯度递减,江苏省各县市EFI空间异质性随时间下降。利用全球Moran指数(Moran's I)和局部空间关联指数(LISA)来分析江苏省各县之间EFI的全球和局部空间相关性。高,低,低,低的集聚效应是最常见的,并且随着时间的推移,强集聚的县对相邻单位产生同化影响。
更新日期:2020-10-28
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