Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon
Regulatory enforcement of forest conservation laws is often dismissed as an ineffective approach to reducing tropical forest loss. Yet, effective enforcement is often a precondition for alternative conservation measures, such as payments for environmental services, to achieve desired outcomes. Fair...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2015
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| Acceso en línea: | https://hdl.handle.net/10568/95063 |
| _version_ | 1855516154139770880 |
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| author | Börner, J. Kis-Katos, K. Hargrave, J. König, K. |
| author_browse | Börner, J. Hargrave, J. Kis-Katos, K. König, K. |
| author_facet | Börner, J. Kis-Katos, K. Hargrave, J. König, K. |
| author_sort | Börner, J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Regulatory enforcement of forest conservation laws is often dismissed as an ineffective approach to reducing tropical forest loss. Yet, effective enforcement is often a precondition for alternative conservation measures, such as payments for environmental services, to achieve desired outcomes. Fair and efficient policies to reducing emissions from deforestation and forest degradation (REDD) will thus crucially depend on understanding the determinants and requirements of enforcement effectiveness. Among potential REDD candidate countries, Brazil is considered to possess the most advanced deforestation monitoring and enforcement infrastructure. This study explores a unique dataset of over 15 thousand point coordinates of enforcement missions in the Brazilian Amazon during 2009 and 2010, after major reductions of deforestation in the region. We study whether local deforestation patterns have been affected by field-based enforcement and to what extent these effects vary across administrative boundaries. Spatial matching and regression techniques are applied at different spatial resolutions. We find that field-based enforcement operations have not been universally effective in deterring deforestation during our observation period. Inspections have been most effective in reducing large-scale deforestation in the states of Mato Grosso and Pará, where average conservation effects were 4.0 and 9.9 hectares per inspection, respectively. Despite regional and actor-specific heterogeneity in inspection effectiveness, field-based law enforcement is highly cost-effective on average and might be enhanced by closer collaboration between national and state-level authorities. |
| format | Journal Article |
| id | CGSpace95063 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace950632025-06-17T08:23:21Z Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon Börner, J. Kis-Katos, K. Hargrave, J. König, K. forest conservation law climate change Regulatory enforcement of forest conservation laws is often dismissed as an ineffective approach to reducing tropical forest loss. Yet, effective enforcement is often a precondition for alternative conservation measures, such as payments for environmental services, to achieve desired outcomes. Fair and efficient policies to reducing emissions from deforestation and forest degradation (REDD) will thus crucially depend on understanding the determinants and requirements of enforcement effectiveness. Among potential REDD candidate countries, Brazil is considered to possess the most advanced deforestation monitoring and enforcement infrastructure. This study explores a unique dataset of over 15 thousand point coordinates of enforcement missions in the Brazilian Amazon during 2009 and 2010, after major reductions of deforestation in the region. We study whether local deforestation patterns have been affected by field-based enforcement and to what extent these effects vary across administrative boundaries. Spatial matching and regression techniques are applied at different spatial resolutions. We find that field-based enforcement operations have not been universally effective in deterring deforestation during our observation period. Inspections have been most effective in reducing large-scale deforestation in the states of Mato Grosso and Pará, where average conservation effects were 4.0 and 9.9 hectares per inspection, respectively. Despite regional and actor-specific heterogeneity in inspection effectiveness, field-based law enforcement is highly cost-effective on average and might be enhanced by closer collaboration between national and state-level authorities. 2015 2018-07-03T11:02:19Z 2018-07-03T11:02:19Z Journal Article https://hdl.handle.net/10568/95063 en Open Access Public Library of Science Börner, J., Kis-Katos, K., Hargrave, J., König, K.. 2015. Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon PLoS ONE, 10 (4) : e0121544. https://doi.org/10.1371/journal.pone.0121544 |
| spellingShingle | forest conservation law climate change Börner, J. Kis-Katos, K. Hargrave, J. König, K. Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon |
| title | Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon |
| title_full | Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon |
| title_fullStr | Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon |
| title_full_unstemmed | Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon |
| title_short | Post-Crackdown Effectiveness of Field-Based Forest Law Enforcement in the Brazilian Amazon |
| title_sort | post crackdown effectiveness of field based forest law enforcement in the brazilian amazon |
| topic | forest conservation law climate change |
| url | https://hdl.handle.net/10568/95063 |
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