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Choosing the best socioeconomic nutrients for the best trees: a discussion about the distribution of Portuguese Trees of Public Interest

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  • Paulo Reis Mourao

    (University of Minho)

  • Vítor Domingues Martinho

    (Polytechnic Institute of Viseu (IPV))

Abstract

Around the world, there are several ecosystems, some in their initial conditions, which are unique and need preservation. This heritage needs adjusted management plans and conservation attentions. It is important to preserve the natural heritage for the future generations, creating active legislation, more governance, empowerment for the related institutions, and bringing more dynamics for these frameworks. However, the literature shows that these issues may be explored deeper. In this context, using data from the Portuguese Institute for Conservation and Nature, this work discusses the heterogeneous distribution of Trees of Public Interest (TPI) in Portugal. These trees are relevant for several criteria such as size, age, or cultural value. Through the use of Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regressions, several conclusions have been reached. Among these conclusions, it is highlighted that areas characterized by higher levels of touristic dynamism tend to increase the number of TPI. Higher percentages of forestry area for intervention ensure at least one TPI. The age of the creation of the municipality also significantly influences the risk of having no TPI.

Suggested Citation

  • Paulo Reis Mourao & Vítor Domingues Martinho, 2021. "Choosing the best socioeconomic nutrients for the best trees: a discussion about the distribution of Portuguese Trees of Public Interest," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5985-6001, April.
  • Handle: RePEc:spr:endesu:v:23:y:2021:i:4:d:10.1007_s10668-020-00858-z
    DOI: 10.1007/s10668-020-00858-z
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    1. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    2. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107611252.
    3. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107028333.
    4. Sun, Yan & Mwangi, Esther & Meinzen-Dick, Ruth Suseela, 2011. "Gender, institutions and sustainability in the context of forest decentralization reforms in Latin America and East Africa," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103456, Agricultural and Applied Economics Association.
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