Enhancing cumulative impact assessment via coproduction of knowledge: Evide

ID: 1580

Presenting Author: Juliana Siqueira-Gay

Session: 519 - Perspectives on assessing cumulative impacts in the Global South

Status: pending


Summary Statement

We debate the importance of developing guidance for assessing cumulative impacts of infrastructure on tropical forests, including traditional knowledge and local sociobiodiversity.


Abstract

Infrastructure projects are well-known drivers of deforestation and cumulative forest loss in tropical regions. Especially the case of roads, several scientific models and tools already evidenced these large-scale impacts. However, it still missing the proper consideration of these cumulative impacts into decisions of planning and project appraisal. This research aims at support further decisions of infrastructure projects in the context of biodiverse and socioenvironmental rich region, the Xingu basin in the Brazilian Amazon. Based on co-production of knowledge principles, the main difficulties and needs to advance CEA practice in Brazil are debated with several stakeholders involved in the planning and project decision making in Brazil. The focus is discussing the role of cumulative impact assessment at different decision levels and provide evidences of spatial data analysis and case studies review. We identified the main pressures of land use and land cover changes in the basin, showing hotspots of soy conversion and spread deforestation surrounding existing roads. The causality of cumulative impacts is discussed in the case of Belo Monte hydroelectric dam to inform further terms of reference of future environmental studies of proposed projects in the region. Our results informs about the importance of developing CEA tools, methods and guidance in the context of tropical forests, especially considering the traditional knowledge and the local sociobiodiversity.


Author Bio

Juliana is a Professor at the University of São Paulo, Brazil, teaching and researching about impact assessment


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