School of Public Administration and Sociology, Jiangsu Normal University, Xuzhou 221116, China.
School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China.
Int J Environ Res Public Health. 2020 Oct 26;17(21):7833. doi: 10.3390/ijerph17217833.
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.
由于江苏省在快速经济发展过程中面临着巨大的生态压力，因此有必要评估其生态足迹强度（EFI）。本文侧重于县级生态足迹强度分析。我们使用县级数据，评估了 1995 年至 2015 年江苏省各县的生态足迹强度的空间分布和时间趋势。根据时间趋势，各县可分为五类：线性递减型、N 型、倒 N 型、U 型和倒 U 型。结果发现，大多数县的碳足迹强度比例保持或增加。探索性空间数据分析表明，EFI 空间分布存在一定的规律，即从北到南逐渐减少，并且随着时间的推移，江苏省各县的 EFI 空间异质性呈下降趋势。全局 Moran 指数（Moran's I）和局部空间关联指数（LISA）用于分析江苏省各县 EFI 的全局和局部空间相关性。高-高和低-低集聚效应最为常见，并且随着时间的推移，具有强烈集聚效应的县对相邻单位具有同化影响。研究结果表明，在相关生态政策中，采用差异化的 EFI 减排控制措施以及促进低-低集聚和抑制高-高集聚是有效的。
