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Naming and Shaming for Conservation: Evidence from the Brazilian Amazon.

Cisneros E, Zhou SL, Börner J - PLoS ONE (2015)

Bottom Line: Farms in blacklisted districts face additional administrative hurdles to obtain authorization for clearing forests.Multiple robustness checks are conducted including an analysis of potential causal mechanisms behind the success of the blacklist.We find that the blacklist has considerably reduced deforestation in the affected districts even after controlling for the potential mechanism effects of field-based enforcement, environmental registration campaigns, and rural credit.

View Article: PubMed Central - PubMed

Affiliation: Zentrum für Entwicklungsforschung, University of Bonn, Bonn, Germany.

ABSTRACT
Deforestation in the Brazilian Amazon has dropped substantially after a peak of over 27 thousand square kilometers in 2004. Starting in 2008, the Brazilian Ministry of the Environment has regularly published blacklists of critical districts with high annual forest loss. Farms in blacklisted districts face additional administrative hurdles to obtain authorization for clearing forests. In this paper we add to the existing literature on evaluating the Brazilian anti-deforestation policies by specifically quantifying the impact of blacklisting on deforestation. We first use spatial matching techniques using a set of covariates that includes official blacklisting criteria to identify control districts. We then explore the effect of blacklisting on change in deforestation in double difference regressions with panel data covering the period from 2002 to 2012. Multiple robustness checks are conducted including an analysis of potential causal mechanisms behind the success of the blacklist. We find that the blacklist has considerably reduced deforestation in the affected districts even after controlling for the potential mechanism effects of field-based enforcement, environmental registration campaigns, and rural credit.

No MeSH data available.


Related in: MedlinePlus

Deforestation in treatment and control districts.Average yearly deforestation levels on the left panel and average change in deforestation on the right panel. Solid lines depict averages of the blacklisted districts (50). The dashed lines show averages of all non-blacklisted districts (442). The dotted lines show averages of the matched control sample (50).
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pone.0136402.g003: Deforestation in treatment and control districts.Average yearly deforestation levels on the left panel and average change in deforestation on the right panel. Solid lines depict averages of the blacklisted districts (50). The dashed lines show averages of all non-blacklisted districts (442). The dotted lines show averages of the matched control sample (50).

Mentions: Fig 3 depicts average deforestation (left panel) and average year-to-year changes in forest loss for blacklisted and non-blacklisted districts during our study period. Average deforestation in blacklisted districts exhibits a much faster decrease than deforestation in untreated districts, but substantial decreases already occurred before the blacklist was enacted in 2008, for example between 2004 and 2005. The right panel of Fig 3 shows that average year-to-year decreases in deforestation were constantly larger in blacklisted than in control districts after 2005.


Naming and Shaming for Conservation: Evidence from the Brazilian Amazon.

Cisneros E, Zhou SL, Börner J - PLoS ONE (2015)

Deforestation in treatment and control districts.Average yearly deforestation levels on the left panel and average change in deforestation on the right panel. Solid lines depict averages of the blacklisted districts (50). The dashed lines show averages of all non-blacklisted districts (442). The dotted lines show averages of the matched control sample (50).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4580616&req=5

pone.0136402.g003: Deforestation in treatment and control districts.Average yearly deforestation levels on the left panel and average change in deforestation on the right panel. Solid lines depict averages of the blacklisted districts (50). The dashed lines show averages of all non-blacklisted districts (442). The dotted lines show averages of the matched control sample (50).
Mentions: Fig 3 depicts average deforestation (left panel) and average year-to-year changes in forest loss for blacklisted and non-blacklisted districts during our study period. Average deforestation in blacklisted districts exhibits a much faster decrease than deforestation in untreated districts, but substantial decreases already occurred before the blacklist was enacted in 2008, for example between 2004 and 2005. The right panel of Fig 3 shows that average year-to-year decreases in deforestation were constantly larger in blacklisted than in control districts after 2005.

Bottom Line: Farms in blacklisted districts face additional administrative hurdles to obtain authorization for clearing forests.Multiple robustness checks are conducted including an analysis of potential causal mechanisms behind the success of the blacklist.We find that the blacklist has considerably reduced deforestation in the affected districts even after controlling for the potential mechanism effects of field-based enforcement, environmental registration campaigns, and rural credit.

View Article: PubMed Central - PubMed

Affiliation: Zentrum für Entwicklungsforschung, University of Bonn, Bonn, Germany.

ABSTRACT
Deforestation in the Brazilian Amazon has dropped substantially after a peak of over 27 thousand square kilometers in 2004. Starting in 2008, the Brazilian Ministry of the Environment has regularly published blacklists of critical districts with high annual forest loss. Farms in blacklisted districts face additional administrative hurdles to obtain authorization for clearing forests. In this paper we add to the existing literature on evaluating the Brazilian anti-deforestation policies by specifically quantifying the impact of blacklisting on deforestation. We first use spatial matching techniques using a set of covariates that includes official blacklisting criteria to identify control districts. We then explore the effect of blacklisting on change in deforestation in double difference regressions with panel data covering the period from 2002 to 2012. Multiple robustness checks are conducted including an analysis of potential causal mechanisms behind the success of the blacklist. We find that the blacklist has considerably reduced deforestation in the affected districts even after controlling for the potential mechanism effects of field-based enforcement, environmental registration campaigns, and rural credit.

No MeSH data available.


Related in: MedlinePlus