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Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia

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  • Eric Nazindigouba Kere

    (CERDI - Centre d'Études et de Recherches sur le Développement International - UdA - Université d'Auvergne - Clermont-Ferrand I - CNRS - Centre National de la Recherche Scientifique)

  • Johanna Choumert

    (CERDI - Centre d'Études et de Recherches sur le Développement International - UdA - Université d'Auvergne - Clermont-Ferrand I - CNRS - Centre National de la Recherche Scientifique)

  • Pascale Combes Motel

    () (CERDI - Centre d'Études et de Recherches sur le Développement International - UdA - Université d'Auvergne - Clermont-Ferrand I - CNRS - Centre National de la Recherche Scientifique)

  • Jean-Louis Combes

    () (CERDI - Centre d'Études et de Recherches sur le Développement International - UdA - Université d'Auvergne - Clermont-Ferrand I - CNRS - Centre National de la Recherche Scientifique)

  • Olivier Santoni

    (CERDI - Centre d'Études et de Recherches sur le Développement International - UdA - Université d'Auvergne - Clermont-Ferrand I - CNRS - Centre National de la Recherche Scientifique)

  • Sonia Schwartz

    (CERDI - Centre d'Études et de Recherches sur le Développement International - UdA - Université d'Auvergne - Clermont-Ferrand I - CNRS - Centre National de la Recherche Scientifique)

Abstract

Using a remotely sensed pixel data set, we develop a multilevel model and propensity score weighting with multilevel data to assess the impact of protected areas on deforestation in the Brazilian Amazon. These techniques allow taking into account location bias, contextual bias and the dependence of spatial units. The results suggest that protected areas have slowed down deforestation between 2005 and 2009, whatever the type of governance. The results also evidence that protected and unprotected areas do not share the same location characteristics. In addition, the effectiveness of protected areas differs according to socioeconomic and environmental variables measured at municipal level.

Suggested Citation

  • Eric Nazindigouba Kere & Johanna Choumert & Pascale Combes Motel & Jean-Louis Combes & Olivier Santoni & Sonia Schwartz, 2016. "Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia," Working Papers halshs-01256600, HAL.
  • Handle: RePEc:hal:wpaper:halshs-01256600
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01256600
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    Cited by:

    1. Amin, A. & Choumert-Nkolo, J. & Combes, J.-L. & Combes Motel, P. & Kéré, E.N. & Ongono-Olinga, J.-G. & Schwartz, S., 2019. "Neighborhood effects in the Brazilian Amazônia: Protected areas and deforestation," Journal of Environmental Economics and Management, Elsevier, vol. 93(C), pages 272-288.
    2. Sébastien Desbureaux, 2016. "Common Resources Management and the "Dark Side" of Collective Action: an Impact Evaluation for Madagascar’s Forests," Working Papers 2016.30, FAERE - French Association of Environmental and Resource Economists.
    3. Tacconi, Luca & Rodrigues, Rafael J. & Maryudi, Ahmad, 2019. "Law enforcement and deforestation: Lessons for Indonesia from Brazil," Forest Policy and Economics, Elsevier, vol. 108(C), pages 1-1.

    More about this item

    Keywords

    Brazilian Legal Amazon; Protected areas; deforestation; Impact analysis.;

    JEL classification:

    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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