Transparency, Sustainable Governance and Digital Accessibility in Municipalities: A Machine Learning Approach

ABSTRACT Achieving the Sustainable Development Goals (SDGs) requires transparent and accountable local governments, yet little is known about the structural drivers of municipal transparency. This study introduces a machine learning approach to predict municipal transparency using the Bidimensional Transparency Index (BTI), which measures both the breadth and depth of information disclosure. Using data from 101 municipalities, we apply an interpretable supervised machine‐learning approach based on Random Forest classification with imbalance‐adjustment techniques. Model performance is assessed in a static setting (2020) and through an out‐of‐period evaluation (2018–2020). The results show that municipal transparency is shaped by the joint influence of fiscal capacity, SDG‐related social and governance indicators, and the accessibility and technical quality of municipal websites. Web accessibility emerges as a key enabler of inclusive transparency rather than a purely technical feature. The study links digital accessibility and SDG‐oriented governance to evidence‐based assessments of municipal transparency.