Geanderson Ambrósio is an applied economist and postdoctoral researcher with eight years’ experience modeling global land-use, biodiversity, and sustainability pathways, having led IMAGE-Land contributions for PICASSO and SHAPE at Utrecht University and PBL. He pairs domain expertise in SDG scenario design and biodiversity metrics (MSA, BII) with hands-on Python tool development that turns complex model outputs into decision-ready dashboards and indicators. His work spans academia, policy, and practice—publishing in top journals while convening multi-stakeholder model intercomparisons that bridge IAM, CGE, and system-dynamics approaches. He also brings entrepreneurial grit from founding a circular logistics startup that cut single-use festival waste and scaled revenue rapidly, demonstrating an ability to prototype and measure real-world sustainability impact. Comfortable in multicultural teams across Brazil, Germany, the USA and the Netherlands, he blends systems thinking with practical data-science implementation, including contributions to the MAgPIE agricultural model for national emissions and land-use policy scenarios.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Applied Economics, Doctor of Philosophy - PhD, Applied Economics at Universidade Federal de Viçosa
High School Diploma, HIGH SCHOOL/SECONDARY DIPLOMAS AND CERTIFICATES, High School Diploma, HIGH SCHOOL/SECONDARY DIPLOMAS AND CERTIFICATES at Colégio Arquidiocesano de Ouro Branco
Bachelor's degree, Economics, Bachelor's degree, Economics at Universidade Federal de São João del-Rei - UFSJ
Model of Agricultural Production and its Impact on the Environment (MAgPIE) - model code
Role in this project:
Back-end Developer / Data Scientist
Contributions:37 commits in 10 months
Contributions summary:Geanderson implemented initial steps for the implementation of the INDC (Intended Nationally Determined Contributions) using CSV files. Their work involved creating scripts to generate CSV files containing policy settings related to deforestation, afforestation, and emissions for both NPI and INDC scenarios. The user also wrote functions to calculate NPI and INDC scenarios based on existing data and MAgPIE model results. This involved reading and manipulating magpie data to prepare input files for the MAgPIE model.
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