A Geospatial AI and Digital Twin Framework for Financial Risk Management in Sustainable Infrastructure Corridors: A Comprehensive Review

The global push for sustainable development is driving unprecedented investment in large-scale infrastructure corridors, such as renewable energy grids, sustainable transport networks, and resilient water systems. While critical for a low-carbon future, these projects present a unique and complex set of financial risks that traditional risk management models, often siloed and reliant on historical data, are ill-equipped to handle. This paper proposes and elaborates on a novel, integrative framework that leverages the convergent power of Geospatial Artificial Intelligence (GeoAI) and Digital Twins to revolutionize financial risk management for sustainable infrastructure corridors. We review the limitations of current financial models in capturing the dynamic, multi-scale, and interconnected risks from climate physical risks and geopolitical tensions to supply chain disruptions and community opposition inherent in these long-lived, place-based assets. The core of the paper delineates the architecture of the proposed framework, detailing how GeoAI ingests and analyzes vast spatiotemporal data (e.g., satellite imagery, IoT sensor feeds, social media data) to create a living, data-rich representation of the corridor. This representation is then operationalized through a financial Digital Twin, a dynamic simulation model that mirrors the physical corridor's behavior and its financial performance in near real-time. We explore specific applications across the project lifecycle, including: enhanced due diligence and site selection, real-time monitoring of construction progress and budget adherence, dynamic forecasting of operational revenues under climate stress, and stress-testing financial resilience against cascading failure scenarios. The paper concludes by discussing the significant implementation challenges data governance, model interoperability, and skills gaps and outlines a future research agenda. This framework promises a paradigm shift from reactive, static financial assessment to a proactive, predictive, and spatially-aware approach, thereby de-risking capital, lowering the cost of financing, and accelerating the deployment of vital sustainable infrastructure.