2017
DOI: 10.1016/j.ecolecon.2017.02.018
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Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia

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 sh… Show more

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Cited by 29 publications
(15 citation statements)
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References 49 publications
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“…This pairing’s relevance is related to the heterogeneity of pressure for vegetation. We assumed that accessibility ( 24 ), agricultural suitability ( 24 ), and the socioeconomic ( 25 ) context are the main factors related to the likelihood that a site will undergo a loss of natural vegetation. Then, we made a preselection of 12 covariates that represent these factors: distance to roads, distance to water bodies, distance to the coast, distance to urban spots, travel time to large cities, rainfall, agricultural potential, elevation, slope, municipal human development index (MHDI), population density, and rural density (see table S2 for detailed description and justification).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…This pairing’s relevance is related to the heterogeneity of pressure for vegetation. We assumed that accessibility ( 24 ), agricultural suitability ( 24 ), and the socioeconomic ( 25 ) context are the main factors related to the likelihood that a site will undergo a loss of natural vegetation. Then, we made a preselection of 12 covariates that represent these factors: distance to roads, distance to water bodies, distance to the coast, distance to urban spots, travel time to large cities, rainfall, agricultural potential, elevation, slope, municipal human development index (MHDI), population density, and rural density (see table S2 for detailed description and justification).…”
Section: Methodsmentioning
confidence: 99%
“…Here, we combined theory and an observational approach using machine learning to find the main determinants of vegetation loss in Brazilian biomes. We assumed that accessibility ( 24 ), agricultural suitability ( 24 ), and the socioeconomic context ( 25 ) are the main factors that influence the pressure for conversion (table S2). On the basis of 12 covariates that represent these factors, we trained random forest models to predict the remaining vegetation in the year 2000 in each biome.…”
Section: Introductionmentioning
confidence: 99%
“…Globally, the illegal use of natural resources is one of the biggest threats to biodiversity, and generally threatens the integrity of PAs and the viability of endangered species ( Conteh, Gavin & Solomon, 2015 ; Dinerstein et al, 2007 ; Gavin, Solomon & Blank, 2010 ; Laurance et al, 2012 ). Despite the fact that Amazonian PAs are one of the most important means of reducing deforestation rates in the biome ( Kere et al, 2017 ), PA creation alone is not sufficient to reduce threats to biological diversity.…”
Section: Discussionmentioning
confidence: 99%
“…Globally, the illegal use of natural resources is one of the biggest threats to biodiversity, and generally threatens the integrity of PAs and the viability of endangered species (Conteh et al 2015;Dinerstein et al 2007;Gavin et al 2010;Laurance et al 2012). Despite the fact that Amazonian PAs are one of the most important means of reducing deforestation rates in the biome (Kere et al 2017), PA creation alone is not sufficient to reduce threats to biological diversity.…”
Section: Discussionmentioning
confidence: 99%