Assessing climate change scenarios in the Amazon Basin: a risk governance model

Abstract

The impacts of climate change are becoming more severe and can only be addressed through governance that spans from local to global levels. To enhance adaptation and response strategies, we need to integrate the knowledge of local communities and improve institutional and state capacity. In the Amazon Basin, current plans for adaptation and response only consider a global perspective, without any input from local communities. By including the region-specific knowledge of these communities, we can better identify the risks related to climate change. The research aimed to answer the question of how to integrate these specificities in an operational risk governance model. We developed an operational risk governance model for Amazon (R-GOMAM) that explores cross-scale interplay and risk identification related to climate change. It provides a comprehensive perspective of risk governance across different scales. A combination of methods was used to integrate the quantitative and qualitative dimensions of data collection and analysis. Additionally, we applied fuzzy logic, to synthesize the cross-scale interplay model. The model was able to account for all dimensions of the Amazon Basin countries, including dryland agriculture, floodplain agriculture, vegetable extraction, fishing, animal breeding, water quality, and household infrastructure. It considered the complexity and uncertainty of risk governance, identified hazard scenarios, and determined the level of risk in the region. Our evaluation of institutional and state capacity in the Purus River Basin revealed insufficient regulations and institutional mechanisms to address climate change risks. Our model identifies different scenarios of hazards and determines the degree of risk in the Amazon Basin countries. Prior models for Brazilian regions overlooked local differences in institutional and state capacities. Our study fills this gap, serving as a supplement for assessing climate change effects in not just Amazonia but other regions as well.

Keywords:

• Amazon
• risk governance model
• climate change
• operational risk governance model for Amazon (R-GOMAM)
• cross-scale interplay
• risk identification

Acknowledgements

Centre of Amazonian Advanced Studies – NAEA, Global Environment Facility – GEFF, CAPES- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Instituto Escolhas

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

GEF Climate Change, Adaptation Capacity and Risk Governance in the Transboundary Purus River Sub-basin Project, CAPES- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior and Instituto Escolhas.