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Ecological Footprint forecasting and estimating using neural networks and DEA

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  • 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.

Suggested Citation

  • Dexiang Wu & Liang Liang, 2009. "Ecological Footprint forecasting and estimating using neural networks and DEA," International Journal of Global Environmental Issues, Inderscience Enterprises Ltd, vol. 9(3), pages 249-258.
    • Handle: RePEc:ids:ijgenv:v:9:y:2009:i:3:p:249-258
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    Cited by:

    1. Dexiang Wu & WeiDan Ding & Ahmed Koubaa & Abdelkader Chaala & CuiCui Luo, 2017. "Robust DEA to assess the reliability of methyl methacrylate-hardened hybrid poplar wood," Annals of Operations Research, Springer, vol. 248(1), pages 515-529, January.

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