Spatiotemporal evolution of urban-agricultural-ecological space in China and its driving mechanism

Journal of Cleaner Production(2022)

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摘要
Coordinating urban development, food security, and ecological protection is a prerequisite for sustainable development. However, with a large population and rapid urbanization, there are still many challenges to optimize the urban-agricultural-ecological space. Here, taking China as a case in point, we explored the dynamic pattern of urban-agricultural-ecological space from 2000 to 2020. The driving mechanism of urban-agricultural-ecological space was simulated with the Maximum Entropy (MaxEnt) Model at both the national and regional scales. The results indicated that about 1/8 of urban-agricultural-ecological space types in mainland China have been changed from 2000 to 2020, most of which is the transformation between agricultural space and ecological space. According to the main type of urban-agricultural-ecological space change, there could be classified into four different zones from the east coast to the west inland in mainland China. As a whole, socioeconomic factors have more contributions to the urban-agricultural-ecological space changes, especially the GDP density for the transformation between agricultural space and ecological space (the relative importance was 61.0%), the proportion of residential area for the transformation between agricultural space and urban space (the relative importance was 38.1%), and the initial land-use type for the transformation between urban space and ecological space (the relative importance was 30.9%). These findings may support the scientifically delineation of urban space, agricultural space, and ecological space (three districts and three lines) in China.
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关键词
Urban-agricultural-ecological space,Sustainable development goals (SDGs),Spatiotemporal evolution,Driving mechanism,Machine learning method,Maximum entropy (MaxEnt) model
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