Neuro-Symbolic Geospatial Intelligence: A Framework for Understanding Nature-Related Risks in the Informal Global South

Nature-related financial risks are increasingly central to global reporting frameworks, yet most small and informal businesses in developing countries remain invisible to existing data systems. Because these enterprises lack formal records and defined map locations, their environmental impact cannot be assessed or incorporated into large-scale risk models. Current AI and satellite-based methods are insufficient for this task: purely connectionist approaches require large labeled datasets that do not exist for informal industrial settings, while language models lack the structured reasoning required for financial compliance. This paper proposes a neuro-symbolic framework in which satellite imagery is combined with a logic-based industrial knowledge graph to infer the activity types and environmental risk profiles of informal enterprises in unmapped urban areas. The approach leverages symbolic rules to compensate for data scarcity while producing transparent, auditable reasoning traces suitable for TNFD LEAP compliance. A secondary application is identified: the same spatial analysis that locates industrial clusters for financial disclosure purposes simultaneously maps environmental exposure zones relevant to maternal health surveillance in climate-vulnerable cities. Rather than presenting experimental results, this work defines the research gap, proposes an operational framework architecture, and outlines a three-phase research agenda