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Modelling biodiversity

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  • Halkos, George

Abstract

This study uses a sample of 71 countries and nonparametric quantile and partial regressions to model a number of threatened species (reptiles, mammals, fish, birds, trees, plants) in relation to various economic and environmental variables (GDPc, CO¬2 emissions, agricultural production, energy intensity, protected areas, population and income inequality). From the analysis and due to high asymmetric distribution of the dependent variables it seems that a linear regression is not adequate and cannot capture properly the dimension of the threatened species. We find that using OLS instead of non-parametric techniques over- or under-estimates the parameters which may have serious policy implications.

Suggested Citation

  • Halkos, George, 2010. "Modelling biodiversity," MPRA Paper 39075, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:39075
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    References listed on IDEAS

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    1. R. K. Turner & Kenneth Button & Peter Nijkamp (ed.), 1999. "Ecosystems and Nature," Books, Edward Elgar Publishing, number 1518.
    2. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    3. R. J. Scholes & R. Biggs, 2005. "A biodiversity intactness index," Nature, Nature, vol. 434(7029), pages 45-49, March.
    4. Costanza, Robert & Fisher, Brendan & Mulder, Kenneth & Liu, Shuang & Christopher, Treg, 2007. "Biodiversity and ecosystem services: A multi-scale empirical study of the relationship between species richness and net primary production," Ecological Economics, Elsevier, vol. 61(2-3), pages 478-491, March.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    6. Rolf Groeneveld & Carla Grashof-Bokdam & Ekko van Ierland, 2005. "Metapopulations in Agricultural Landscapes: A Spatially Explicit Trade-off Analysis," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 48(4), pages 527-547.
    7. Samuel Brody, 2003. "Examining the Effects of Biodiversity on the Ability of Local Plans to Manage Ecological Systems," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 46(6), pages 817-837.
    8. Gregory M Mikkelson & Andrew Gonzalez & Garry D Peterson, 2007. "Economic Inequality Predicts Biodiversity Loss," PLOS ONE, Public Library of Science, vol. 2(5), pages 1-5, May.
    9. Halkos, George E., 2003. "Environmental Kuznets Curve for sulfur: evidence using GMM estimation and random coefficient panel data models," Environment and Development Economics, Cambridge University Press, vol. 8(4), pages 581-601, October.
    10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    11. Nunes, Paulo A. L. D. & van den Bergh, Jeroen C. J. M., 2001. "Economic valuation of biodiversity: sense or nonsense?," Ecological Economics, Elsevier, vol. 39(2), pages 203-222, November.
    12. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    13. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    14. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    15. Georgina M. Mace, 2005. "An index of intactness," Nature, Nature, vol. 434(7029), pages 32-33, March.
    16. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
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    Cited by:

    1. Paunić, Alida, 2016. "Brazil, Preservation of Forest and Biodiversity," MPRA Paper 71462, University Library of Munich, Germany.

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    More about this item

    Keywords

    Nonparametric quantile regression; biodiversity;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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