A Sustainability-Oriented NLP Framework for Early Detection of Economic, Operational, and Environmental Risks in Global Shipping

The global shipping industry faces escalating sustainability risks arising from geopolitical disruptions, operational instability, and tightening environmental regulations. These risks often first emerge in qualitative market narratives, limiting the effectiveness of conventional backward-looking indicators. This study proposes a sustainability-oriented natural language processing (NLP) framework for the early detection of sustainability-critical stress in global shipping. Using 155 weekly expert-curated shipping market reports published between 2022 and 2025, the framework integrates topic modeling and domain-tuned sentiment analysis to extract sustainability-relevant signals from unstructured text. Critical-to-Quality (CTQ) factors are reconceptualized as sustainability-critical performance dimensions encompassing economic sustainability (freight rate stability), operational sustainability (schedule reliability, lead time, vessel utilization, and equipment availability), and environmental sustainability (eco-efficiency). Topic–sentiment interactions are quantified using network analysis and ElasticNet-based estimation to construct composite CTQScores, which capture the intensity and persistence of sustainability stress. Empirical validation using observed market performance indicators demonstrates that the CTQScores exhibit strong directional accuracy and systematically precede market adjustments, supporting their role as early warning indicators rather than predictive forecasts. The framework is operationalized as a Sustainability Risk Radar, enabling proactive monitoring of economic, operational, and environmental risks. The findings demonstrate how NLP-based analytics can support ESG-aligned sustainability risk monitoring and resilience-oriented decision-making in global shipping systems.