Relative Eco-Efficiency Recognition Based on DEA Neural Network

Feng Rong Zhang; Yong Hong Cao; Zhuo Li; Xin Wei Xiao

doi:10.4028/www.scientific.net/AMR.622-623.1456

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 622-623Relative Eco-Efficiency Recognition Based on DEA...

Relative Eco-Efficiency Recognition Based on DEA Neural Network

Abstract:

Regional eco-efficiency is regarded as a relative concept. It could be presented the relative eco-efficiency with the change of economy and environment. That means it could not the optimal compared with the predicted optimum in the future. It would be a basis of policy decision making of local governments according to identify the life cycle stage of current regional eco-efficiency. As a beneficial supplement of the eco-efficiency measurement, this paper set up a DEA Neural Network recognition model which can effectively analyze and identify the current relative eco-efficiency of a regional. The empirical research shows that the phenomenon of forecasted value “floating upon” has been restrained effectively. It is an obvious advantage that this method is showed as a method of fast convergence, accurate identification, extensive application and promotion value in practice.

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Periodical:

Advanced Materials Research (Volumes 622-623)

Pages:

1456-1461

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.622-623.1456

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Online since:

December 2012

Authors:

Feng Rong Zhang, Yong Hong Cao, Zhuo Li, Xin Wei Xiao

Keywords:

Data Envelopment Analysis (DEA), Neural Network (NN), Recognition, Relative Eco-Efficiency

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