AI-Driven Sustainability Governance Capacity in Mongolia: A Hybrid Institutional, NLP and Predictive Analytics Framework

Abstract

The management of sustainability transition in Mongolia has significant governance-related challenges arising from the vulnerability to climate change, reliance on mineral resources, air pollution, land degradation and institutional fragmentation. National commitments for sustainability are in place, but implementation is hampered by poor institutional coordination, lack of technical know-how, and weak environmental monitoring and enforcement. While most of the previous sustainability governance research in emerging economies is of a qualitative nature, it provides scant predictive and computational analyses. The aim of this study is to combine the institutional assessment with Governance Capacity Index (GCI) with Natural Language Processing (NLP) and predictive analytics as a hybrid sustainability governance framework to assess the sustainability governance effectiveness in Mongolia. The study takes a mixed methods explanatory design where it explores five dimensions of governance: strategic vision, coordination mechanisms, technical expertise, digital monitoring and enforcement effectiveness. These documents are then analyzed using Python-based NLP techniques like keyword mapping, topic modeling, sentiment analysis, and policy coherence assessment in policy documents, sustainability reports, and governance datasets. Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to explore the relationships between governance capacity and sustainability performance, and machine learning methods used to determine key governance predictors. The results will point to Mongolia's commitment to sustainability, but a lack of coordination, capacity to enforce and a lack of technical expertise limit the effectiveness of governance. Digital monitoring systems and effectiveness of enforcement are expected to become the most important factors to influence sustainability performance. This study brings in a new (and transferable) governance assessment framework for sustainability assessment in emerging economies dependent on resources.