Integrating biofuels and land use dynamics into long-term energy planning

• Grant number: 25/18813-5
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: December 01, 2025
• End date: March 01, 2026
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Fernando Ramos Martins
• Grantee: Paula Conde Santos Borba
• Supervisor: Johannes Schmidt
• Host Institution: Instituto do Mar (IMar). Universidade Federal de São Paulo (UNIFESP). Campus Baixada Santista. Santos , SP, Brazil
• Institution abroad: Universität für Bodenkultur Wien, Austria
• Associated to the scholarship: 24/02007-7 - Multiple near-optimal solutions for the long-term sustainable energy development, BP.PD

Abstract

Brazil is crucial to tackling climate change and halting biodiversity loss. However, dependingon the energy transition pathway, large energy infrastructures may cause negative consequencessuch as land use conflicts and deforestation. Also, climate change can affect thepotential of renewable energy, resulting in the inappropriate deployment of new infrastructuresat certain locations. To comprehensively explore these complexities, we propose utilizingthe 'modeling to generate alternatives' (MGA) method embedded within Calliope, a linearprogramming-based energy model that minimizes the cost. MGAexplores the alternatives nearthe single optimal solution, not exceeding 5% from the least cost. This work forms part of abroader FAPESP-funded project and aims to be partially developed at BOKU University. Theinitial phase will involve the building of long-term scenarios and the development of the BiofuelBrazil-Calliope model. We will then assess the impacts of climate change on precipitationpatterns and the corresponding irrigation demands of expanded biofuel crops by comparinghistorical and projected climate data downloaded from the Copernicus database. Further, wewill analyze potential land-use conflicts arising from such expansion by using georeferenceddata from MapBiomas. Finally, the resulting scenario outputs will be used to train decision tree algorithms to identify robust policy pathways and trade-offs, providing actionable insights forsustainable energy and land-use planning.