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A neuro-computational intelligence analysis of the ecological footprint of nations


  • Mostafa, Mohamed M.
  • Nataraajan, Rajan


The per capita ecological footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the Earth's biological capacity required to support human activity. This study uses three neuro-computational methodologies: multi-layer perceptron neural network (MLP), probabilistic neural network (PNN) and generalized regression neural network (GRNN) to predict and classify the EF of 140 nations. Accuracy indices are used to assess the prediction and classification accuracy of the three methodologies. The study shows that neuro-computational models outperform traditional statistical techniques such as regression analysis and discriminant analysis in predicting and classifying per capita EF due to their robustness and flexibility of modeling algorithms.

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  • Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:9:p:3516-3531

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    References listed on IDEAS

    1. White, Thomas J., 2007. "Sharing resources: The global distribution of the Ecological Footprint," Ecological Economics, Elsevier, vol. 64(2), pages 402-410, December.
    2. Gorr, Wilpen L. & Nagin, Daniel & Szczypula, Janusz, 1994. "Comparative study of artificial neural network and statistical models for predicting student grade point averages," International Journal of Forecasting, Elsevier, vol. 10(1), pages 17-34, June.
    3. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    4. Bagliani, Marco & Bravo, Giangiacomo & Dalmazzone, Silvana, 2008. "A consumption-based approach to environmental Kuznets curves using the ecological footprint indicator," Ecological Economics, Elsevier, vol. 65(3), pages 650-661, April.
    5. J. Stuart McMenamin & Frank A. Monforte, 1998. "Short Term Energy Forecasting with Neural Networks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 43-61.
    6. Kuldeep Kumar & Sukanto Bhattacharya, 2006. "Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances," Review of Accounting and Finance, Emerald Group Publishing, vol. 5(3), pages 216-227, August.
    7. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 71-88, August.
    8. Susmita Dasgupta & Benoit Laplante & Hua Wang & David Wheeler, 2002. "Confronting the Environmental Kuznets Curve," Journal of Economic Perspectives, American Economic Association, vol. 16(1), pages 147-168, Winter.
    9. Andersson, Jan Otto & Lindroth, Mattias, 2001. "Ecologically unsustainable trade," Ecological Economics, Elsevier, vol. 37(1), pages 113-122, April.
    10. van Vuuren, D. P. & Smeets, E. M. W., 2000. "Ecological footprints of Benin, Bhutan, Costa Rica and the Netherlands," Ecological Economics, Elsevier, vol. 34(1), pages 115-130, July.
    11. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    12. McDonald, Garry W. & Patterson, Murray G., 2004. "Ecological Footprints and interdependencies of New Zealand regions," Ecological Economics, Elsevier, vol. 50(1-2), pages 49-67, September.
    13. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    14. Shouhong Wang, 1995. "The Unpredictability of Standard Back Propagation Neural Networks in Classification Applications," Management Science, INFORMS, vol. 41(3), pages 555-559, March.
    15. Sam Mirmirani & H.C. Li, 2004. "Gold Price, Neural Networks and Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 23(2), pages 193-200, March.
    16. Moreno, David & Marco, Paulina & Olmeda, Ignacio, 2006. "Self-organizing maps could improve the classification of Spanish mutual funds," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1039-1054, October.
    17. Mohamed M. Mostafa, 2004. "Forecasting the Suez Canal traffic: a neural network analysis," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(2), pages 139-156, April.
    18. Karen Ehrhardt-Martinez & Edward M. Crenshaw & J. Craig Jenkins, 2002. "Deforestation and the Environmental Kuznets Curve: A Cross-National Investigation of Intervening Mechanisms," Social Science Quarterly, Southwestern Social Science Association, vol. 83(1), pages 226-243.
    19. Shunsuke Managi, 2006. "Pollution, natural resource and economic growth: an econometric analysis," International Journal of Global Environmental Issues, Inderscience Enterprises Ltd, vol. 6(1), pages 73-88.
    20. Hornborg, Alf, 1998. "Towards an ecological theory of unequal exchange: articulating world system theory and ecological economics," Ecological Economics, Elsevier, vol. 25(1), pages 127-136, April.
    21. Grossman, Gene M. & Krueger, Alan B., 1996. "The inverted-U: what does it mean?," Environment and Development Economics, Cambridge University Press, vol. 1(01), pages 119-122, February.
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    Cited by:

    1. Tausch, Arno, 2011. "Costa Rica, superstar? some reflections on the global drivers and bottlenecks of the happy planet index," MPRA Paper 33226, University Library of Munich, Germany.
    2. Tausch, Arno, 2016. "‘Smart development’. An essay on a new political economy of the environment," MPRA Paper 70204, University Library of Munich, Germany.
    3. Tausch, Arno, 2011. "The ‘four economic freedoms’ and life quality. General tendencies and some hard lessons for EU-27-Europe," MPRA Paper 33225, University Library of Munich, Germany.
    4. Arno Tausch & Almas Heshmati, 2012. "Migration, Openness and the Global Preconditions of "Smart Development"," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 26(2), pages 1-62.
    5. Tausch, Arno, 2011. "Globalization as a driver or bottleneck for sustainable development. General tendencies and European implications," MPRA Paper 33227, University Library of Munich, Germany.

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