Spatiotemporal pattern and driving forces of ecological carrying capacity during urbanization process in the Dongting Lake area, China. (November 2022)

• Record Type: Journal Article
• Authors: Xiong, Jianxin; Wang, Xinbin; Zhao, Di; Zhao, Yayuan
• Is Part Of: Ecological indicators. Volume 144(2023)
• Publication Date: 2022-11
• Subjects: Ecological carrying capacity (ECC) -- Spatiotemporal pattern -- Driving force -- Urbanization -- Dongting lake area in China; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.109486
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Ecological elasticity, resource and environmental carrying capacity compose evaluation. ECC has appeared a fluctuating downward trend in Dongting Lake area in 2001–2019. Rapid urbanization has caused overloading of ECC in Dongting Lake area. Spatiotemporal pattern of ECC has shown higher heterogeneous in Dongting Lake area. Main driving factors are population, economy, land, technology and policies. Abstract: Ecological carrying capacity (ECC) is a key indicator for assessing the sustainable urbanization. The Dongting Lake area, as a national eco-economic zone, is urgently required to focus on the spatiotemporal pattern of ECC based on interactive mechanism of urbanization and ECC, which is revealed through quantitative research method in 2001–2019. The results show the ECC value appeared a fluctuating downward trend from 1.4645 to 0.8527, and the bearing state changed from under-loading to overloading. The bearing states of 17 counties showed spatially higher heterogeneous with Moran's I value from 0.2635 to 0.3194. Yuanjiang City, Nanxian County and Taojiang County were in an under-loading state and the remaining 14 counties in an overloading state. The spatial distribution pattern displayed an "M"-type spatial differentiation pattern from southwest to northeast, the central region was evidently better than the surrounding regions. The bearing state had a positive global spatial autocorrelation. Spatial distribution of hot spots showed that the central area was a cold spot area, the edge area was a hot spot area, and the peripheral area was a sub-cold and sub-hot area. Factor detector results reveal that the urban population concentration, economic growth, land expansion, technology levels, and government policies were the main driving factors that formed the spatiotemporal pattern of ECC. The results could make up for the research shortcomings of spatiotemporal pattern of ECC, provide support to control ecological protection red line and urban exploitation boundaries, and help Chinese government formulate effective measurements to optimize the spatiotemporal pattern of ECC.