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Ecological Footprint forecasting and estimating using neural networks and DEA

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Author Info
Dexiang Wu
Liang Liang
Abstract

There is a growing consensus that social and economic sustainability depends on limited natural capital. Ecological Footprint (EF) provides an alternative tool to account for natural capital. This study presents two models to research Wuhan's natural capital: first using Genetic Algorithm Neural Networks (GANN) model to forecast the EF; second, employing the DEA model to estimate the ecosystem effectiveness across different years. Case study is conducted for a big Chinese city where favourable computation is yielded.

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File URL: http://inderscience.metapress.com/link.asp?target=contribution&id=6883045HX8271722
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Publisher Info
Article provided by Inderscience Enterprises Ltd in its journal International Journal of Global Environmental Issues.

Volume (Year): 9 (2009)
Issue (Month): 3 (January)
Pages: 249-258
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Handle: RePEc:mes:ijgenv:v:9:y:2009:i:3:p:249-258

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Web page: http://inderscience.metapress.com/link.asp?target=journal&id=110856

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords: ecological footprint; evaluation; DEA; data envelopment analysis; forecasting; sustainability; sustainable development; China; natural capital; genetic algorithms; neural networks; ecosystems;

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This page was last updated on 2009-12-19.


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