The 2012 Brazilian Forest Code governs the fate of forests and savannas on Brazil's 394 Mha of privately owned lands. The government claims that a new national land registry (SICAR), introduced under the revised law, could end illegal deforestation by greatly reducing the cost of monitoring, enforcement, and compliance. This study evaluates that potential, using data from state-level land registries (CAR) in Pará and Mato Grosso that were precursors of SICAR. Using geospatial analyses and stakeholder interviews, we quantify the impact of CAR on deforestation and forest restoration, investigating how landowners adjust their behaviors over time. Our results indicate rapid adoption of CAR, with registered properties covering a total of 57 Mha by 2013. This suggests that the financial incentives to join CAR currently exceed the costs. Registered properties initially showed lower deforestation rates than unregistered ones, but these differences varied by property size and diminished over time. Moreover, only 6% of registered producers reported taking steps to restore illegally cleared areas on their properties. Our results suggest that, from the landowner's perspective, full compliance with the Forest Code offers few economic benefits. Achieving zero illegal deforestation in this context would require the private sector to include full compliance as a market criterion, while state and federal governments develop SICAR as a de facto enforcement mechanism. These results are relevant to other tropical countries and underscore the importance of developing a policy mix that creates lasting incentives for sustainable land-use practices.
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http://dx.doi.org/10.1073/pnas.1604768114 | DOI Listing |
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School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. Electronic address:
Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke identification in remote sensing images is vital for effective forest fire monitoring within Internet of Things (IoT) systems. However, existing detection methods frequently falter in complex real-world scenarios, where variable smoke shapes and sizes, intricate backgrounds, and smoke-like phenomena (e.
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