{"ID":2866925,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18633","arxiv_id":"2509.18633","title":"Modelling Cascading Physical Climate Risk in Supply Chains with Adaptive Firms: A Spatial Agent-Based Framework","abstract":"We present an open-source Python framework for modelling cascading physical climate risk in a spatial supply-chain economy. The framework integrates geospatial flood hazards with an agent-based model of firms and households, enabling simulation of both direct asset losses and indirect disruptions propagated through economic networks. Firms adapt endogenously through two channels: capital hardening, which reduces direct damage, and backup-supplier search, which mitigates input disruptions. In an illustrative global network, capital hardening reduces direct losses by 26%, while backup-supplier search reduces supplier disruption by 48%, with both partially stabilizing production and consumption. Notably, firms that are never directly flooded still bear a substantial share of disruption, highlighting the importance of indirect cascade effects. The framework provides a reproducible platform for analyzing systemic physical climate risk and adaptation in economic networks.","short_abstract":"We present an open-source Python framework for modelling cascading physical climate risk in a spatial supply-chain economy. The framework integrates geospatial flood hazards with an agent-based model of firms and households, enabling simulation of both direct asset losses and indirect disruptions propagated through eco...","url_abs":"https://arxiv.org/abs/2509.18633","url_pdf":"https://arxiv.org/pdf/2509.18633v4","authors":"[\"Yara Mohajerani\"]","published":"2025-09-23T04:33:58Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"q-fin.RM\"]","methods":"[]","has_code":false}