A Comprehensive Evaluation of Urban Sustainable Development in China Based on the TOPSIS-Entropy Method
By Lin Ding, Zhenfeng Shao, Hanchao Zhang, Congjiang Xu, Dewen Wu. 2016
180 citations recorded by the source corpus.
The Chinese government started an important project “Geographical conditions monitoring (GCM)” in 2012, which aims at fully revealing the spatial pattern of natural resources, and economic and social development. Urban development assessment and analysis are an important topic of the comprehensive statistics in GCM project. This study quantitatively evaluates the sustainable development level of 287 cities at prefecture level and above in China and analyzes their spatial distribution. Both an index system of urban sustainable development (USI) constructed by three aspects, namely society, economy and environment, and the TOPSIS-Entropy method are adopted. The results show that the overall level of urban sustainable development in China is not high, and obvious differences in urban sustainable development among cities exist. The coordination of social, economic and environmental sustainable development in China is at a low level. In terms of spatial distribution, cities from eastern coastal areas of China are at relatively higher levels of sustainable development with central and northeastern cities ranking second and western ones lowest. Several spatial clusters of urban sustainable development can be found. The “High–High” clusters, in which cities have high levels of sustainable development, are distributed in urban agglomerations. Finally, some suggestions have been provided to achieve all-round sustainable development in China.
Subfields: Economics and Econometrics, Global and Planetary Change, Transportation
View Paper: https://www.semanticscholar.org/paper/5358c8b43442e6395c86425417bae2583ccef857
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Citations in the corpus
- Examining the Coordination Between Green Finance and Green Economy Aiming for Sustainable Development: A Case Study of China (2020, methodology): “The entropy method has been extensively adopted to determine the weights of environmental and economic indicators [44,45].”