Sustainability analysis of the urban ecosystem in Guangzhou City based on information entropy between 2004 and 2010

doi:10.1007/s11442-013-1019-5

Abstract

Abstract:

The urban ecosystem possesses dissipating structures that can absorb substances and energy from the external environment and export products and wastes to maintain order within the system. Given these circumstances, this paper analyzed the ability of the urban ecosystem in Guangzhou City to sustain development from the perspective of entropy. The research was carried out in three steps. First, an evaluation index system that considers the ability of the urban ecosystem for sustainable development was formed based on the structures and functions of the urban ecosystem and the change in the entropy of the urban socioeconomic ecosystem. Second, the sustainable development ability assessment model for the urban ecosystem was built using information entropy. Last, by combining the time series variation of the evaluation indicators with the entropy weights, this paper analyzed the influence of the combined factors on the sustainable development ability of the urban ecosystem in Guangzhou and suggested some measures to promote the sustainable development of the urban ecosystem in Guangzhou. The conclusions of this study can be summarized as follows: (1) The urban ecosystem has developed in an orderly and healthy direction, with effective control over the urban environmental pollution problems in Guangzhou between 2004 and 2010. (2) The sustainable development ability of the urban ecosystem had been on an upward trend in Guangzhou during the study period. The ability of the natural urban ecosystem to support the urban socioeconomic ecosystem increased continuously, and the improved ecoenvironment enhanced the harmony and vitality of the urban ecosystem in Guangzhou.

Key words: urban ecosystem, entropy change, information entropy, sustainable development, Guangzhou City

LIN Zhenming, XIA Bin. Sustainability analysis of the urban ecosystem in Guangzhou City based on information entropy between 2004 and 2010[J].Journal of Geographical Sciences, 2013, 23(3): 417-435.

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