Modeling Climate-Health Co-Benefits of Livestock Mitigation in Europe: Linking Greenhouse Gas Emissions to Antimicrobial Use Through Statistical, Geospatial, and Machine Learning Analysis

The dual crises of climate change and antimicrobial resistance (AMR) are increasingly recognized as interconnected challenges, particularly within the livestock sector, which contributes significantly to both greenhouse gas (GHG) emissions and antimicrobial use (AMU). This thesis explores the potential climate–health co-benefits of livestock mitigation in Europe by examining the relationship between GHG emissions and AMU through an interdisciplinary modeling approach. Drawing on multiple international datasets, including FAOSTAT, ESVAC, and recent projections from AMU, the study employs statistical regression, machine learning, and geospatial analysis to examine spatial patterns, temporal trends, and predictive linkages. The analysis further incorporates scenario modeling to evaluate the impact of production shifts and policy interventions on environmental and health outcomes. Positioned within the One Health framework, this research aims to support integrated policymaking by identifying opportunities for simultaneous reductions in environmental burden and public health risks associated with intensive livestock systems.