Artigo Acesso aberto Produção Nacional Revisado por pares

Limiting the high impacts of Amazon forest dieback with no-regrets science and policy action

2018; National Academy of Sciences; Volume: 115; Issue: 46 Linguagem: Inglês

10.1073/pnas.1721770115

ISSN

1091-6490

Autores

David M. Lapola, Patrícia Pinho, Carlos A. Quesada, Bernardo B. N. Strassburg, Anja Rammig, Bart Kruijt, Foster Brown, Jean Pierre Ometto, Adriano Premebida, José Marengo, Walter Vergara, Carlos A. Nobre,

Tópico(s)

Ecosystem dynamics and resilience

Resumo

Large uncertainties still dominate the hypothesis of an abrupt large-scale shift of the Amazon forest caused by climate change [Amazonian forest dieback (AFD)] even though observational evidence shows the forest and regional climate changing. Here, we assess whether mitigation or adaptation action should be taken now, later, or not at all in light of such uncertainties. No action/later action would result in major social impacts that may influence migration to large Amazonian cities through a causal chain of climate change and forest degradation leading to lower river-water levels that affect transportation, food security, and health. Net-present value socioeconomic damage over a 30-year period after AFD is estimated between US dollar (USD) $957 billion (×10 9 ) and $3,589 billion (compared with Gross Brazilian Amazon Product of USD $150 billion per year), arising primarily from changes in the provision of ecosystem services. Costs of acting now would be one to two orders of magnitude lower than economic damages. However, while AFD mitigation alternatives—e.g., curbing deforestation—are attainable (USD $64 billion), their efficacy in achieving a forest resilience that prevents AFD is uncertain. Concurrently, a proposed set of 20 adaptation measures is also attainable (USD $122 billion) and could bring benefits even if AFD never occurs. An interdisciplinary research agenda to fill lingering knowledge gaps and constrain the risk of AFD should focus on developing sound experimental and modeling evidence regarding its likelihood, integrated with socioeconomic assessments to anticipate its impacts and evaluate the feasibility and efficacy of mitigation/adaptation options.

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