Integrating Geo AI-Driven Supply Chain Ecosystems and Environmental Consid erations into Urban Strategic Planning
Abstract:
The rapid pace of urbanization combined with climate change challenges has increased the need for resilient and en vironmentally sustainable cities. This research explores the integration of Geospatial Artificial Intelligence (Geo-AI) within supply chain ecosystems to enhance climate-responsive strategic urban planning. Geo-AI enables advanced spatial analytics, predictive modeling, and dynamic optimization of urban supply chains, improving environmental outcomes such as emissions reductions, resource efficiency, and disaster resilience. Using mixed methods—com prising spatial data analysis, system simulations, and case applications—this study demonstrates how Geo-AI can bridge urban planning, supply chain management, and environmental sustainability. Results reveal that Geo-AI can significantly improve real-time decision-making, optimize routing to lower carbon footprints, and support adaptive land-use planning under climate uncertainty. The paper concludes with recommendations for policymakers and urban planners to operationalize Geo-AI-driven supply chain frameworks within climate-centric urban strategies.
