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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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  • Kere, Eric Nazindigouba
  • Choumert, Johanna
  • Combes Motel, Pascale
  • Combes, Jean Louis
  • Santoni, Olivier
  • Schwartz, Sonia

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. Our results show that the hierarchical structure of the database matters and should be considered in the assessment of protected areas effectiveness. Our results also suggest that protected areas have slowed down deforestation between 2005 and 2009, whatever the type of governance. The effectiveness of protected areas differs according to socioeconomic and environmental variables measured at municipal level. For instance, indigenous protected areas are found to be marginally more efficient than sustainable use areas and integral use areas. Protected Areas that were more recently implemented are also found to avoid more deforestation than older ones. This corroborates the idea that recently created protected areas in the Brazilian Amazon have a greater agricultural potential.

Suggested Citation

  • Kere, Eric Nazindigouba & Choumert, Johanna & Combes Motel, Pascale & Combes, Jean Louis & Santoni, Olivier & Schwartz, Sonia, 2017. "Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia," Ecological Economics, Elsevier, vol. 136(C), pages 148-158.
    • Handle: RePEc:eee:ecolec:v:136:y:2017:i:c:p:148-158
    DOI: 10.1016/j.ecolecon.2017.02.018
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    1. Keles, Derya & Delacote, Philippe & Pfaff, Alexander & Qin, Siyu & Mascia, Michael B., 2020. "What Drives the Erasure of Protected Areas? Evidence from across the Brazilian Amazon," Ecological Economics, Elsevier, vol. 176(C).
    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. 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.
    4. Apeti, Ablam Estel & N’Doua, Bossoma Doriane, 2023. "The impact of timber regulations on timber and timber product trade," Ecological Economics, Elsevier, vol. 213(C).
    5. Li, Yi & Song, Zhenjiang, 2022. "Have protected areas in China achieved the ecological and economic “win-win” goals? Evidence from the Giant Panda Reserves of the Min Mont Range," Forest Policy and Economics, Elsevier, vol. 144(C).
    6. Mathias Reynaert & Edouardo Souza-Rodrigues & Arthur van Benthem, 2023. "The Environmental Impacts of Protected Area Policy," Post-Print hal-04448711, HAL.
    7. 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.

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    More about this item

    Keywords

    Brazilian Legal Amazon; Protected Areas; Deforestation; Impact Analysis;
    All these keywords.

    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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