Within the context of land use and land cover change, deforestation and forest fragmentation are considerable challenges of both local and global concern. Varying governance strategies and conservation programs have resulted in vastly diverse conservation outcomes, which have proved insufficient in mitigating forest loss across multiple scales. This Pursuit seeks to better understand the relationship between forest loss and local-scale governance by assessing indicators of institutional quality at the subnational scale for 772 municipalities across the Brazilian Amazon. Our proposed research investigates the hypothesis that institutional quality, when assessed at the municipality scale with a robust set of indicators, will improve predictive models of deforestation rates and fragmentation indices. By incorporating governance-adjusted deforestation rates in a deforestation model, we can (1) evaluate the effectiveness of governance indicators in improving model accuracy, and (2) simulate deforestation projections under a variety of governance scenarios. We propose a spatial modeling approach that extends existing econometric approaches to integrate governance indicator variables in analyzing deforestation trends. To prepare a robust set of governance covariates at the municipality scale, we will implement a mixed-methods approach, leveraging both qualitative and quantitative data sources. We will evaluate which governance indicators are most important in determining forest conservation outcomes by calibrating forest models with institutional schemes. This pursuit will improve broader understanding on the role of subnational governance in forest conservation and modeling, overcoming disciplinary barriers by working alongside practitioners, governments, and researchers in alleviating deforestation within an integrated socio-environmental forest system.