Deforestation policies do not reduce forest degradation in the Brazilian Amazon

What is it about?

This study examines whether policies aimed at reducing deforestation in the Brazilian Amazon are also effective at limiting forest degradation. While deforestation refers to the complete clearing of forests, degradation includes more subtle damage—such as logging, fires, and fragmentation—that weakens forests without fully removing them. These forms of damage are widespread and can significantly affect biodiversity and climate by releasing stored carbon. Using high-resolution data, we show that although deforestation-focused policies have helped reduce large-scale forest clearing in some areas, they have not led to similar reductions in degradation. In fact, degradation remains extensive and, in some cases, is increasing. This suggests that policies narrowly targeting deforestation may unintentionally overlook other important drivers of forest damage. This is because different processes drive deforestation and degradation. While deforestation is often linked to large-scale agriculture, degradation can result from selective logging, fires, and infrastructure expansion, which are harder to detect and regulate.

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Photo by Karsten Winegeart on Unsplash

Photo by Karsten Winegeart on Unsplash

Why is it important?

The findings are important because degradation can contribute as much—or even more—to carbon emissions than deforestation. Previous research indicates that degradation processes account for a large share of carbon loss in the Amazon, meaning that focusing only on deforestation underestimates the true environmental impact. We argue that conservation strategies in the Amazon and beyond need to go beyond preventing forest clearing. Effective protection of nature and the climate will require policies that also address forest degradation, improve monitoring, and target a wider range of human activities affecting forest health. This study is particularly novel because we measure the impacts of four different policies at the same time using similar methods, allowing for robust comparisons across policies.

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