长江
中国
弹性(材料科学)
地理
生态学
心理弹性
环境科学
生物
心理学
热力学
物理
考古
心理治疗师
作者
Zhixiang Yin,Tiantian Ma,Yanlin Sun,Zongyi Yin
标识
DOI:10.1016/j.iref.2024.103384
摘要
Urban ecology is continuously suffering from risky impacts, and the enhancement of urban ecological resilience has become the basis of practice for addressing environmental challenges and responding to green development. The entropy weight method was used to measure the ecological resilience level of 28 cities in the middle reaches of the Yangtze River in China from 2008 to 2018, the Theil index decomposition method was used to measure the differences in resilience, the exploratory spatial data analysis method revealed its evolution law, and the obstacle model was used for the identification of influencing factors. The findings suggest that: (1) The level of ecological resilience of Chinese cities is generally on a fluctuating upward trend, and the gap between different provinces is gradually decreasing. (2) The spatial distribution of urban ecological resilience is characterized by a "central" feature, with provincial capitals generally higher than other regions, Hubei as a whole being characterized by "high in the east and low in the west" and "eccentricity", while Hunan and Jiangxi have a "diffusion effect". The spatial evolution stage can be divided into the period of ecological resilience impact and the overall enhancement period. (3) The area of green space in parks, the level of greening in built-up areas and the level of comprehensive utilization of industrial solid waste in general have always been the most important barriers to the level of ecological resilience of cities. In addition, factors such as the sound treatment of domestic waste, the treatment of respirable fine particulate matter, sewage treatment and the control of industrial smoke and dust in the city should not be overlooked. Based on this, policy recommendations such as promoting regional collaboration to reduce inter-provincial differences and implementing precise policies to address the main obstacles are proposed.
科研通智能强力驱动
Strongly Powered by AbleSci AI