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Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia
Abstract / Résumé : 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.
Code JEL : C21 - Cross-Sectional Models - Spatial Models - Treatment Effect Models - Quantile Regressions , Q23 - Forestry , Q28 - Government Policy , Q57 - Ecological Economics: Ecosystem Services - Biodiversity Conservation - Bioeconomics - Industrial EcologyPublications :
- [ 2017 ] Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia