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.

翻译：我们提出一个开源Python框架，用于对空间供应链经济中的物理气候风险级联效应进行建模。该框架将地理空间洪水灾害与基于智能体的企业-家庭模型相结合，能够模拟通过经济网络传播的直接资产损失与间接中断效应。企业通过两条内生适应渠道实现调整：资本加固（降低直接损失）和后备供应商搜寻（缓解投入中断）。在示例性全球网络中，资本加固使直接损失降低26%，后备供应商搜寻使供应商中断减少48%，两者均能部分稳定生产与消费。值得注意的是，从未直接遭受洪水侵袭的企业仍承担了相当比例的中断损失，凸显了间接级联效应的重要性。该框架为分析经济网络中系统性的物理气候风险及其适应策略提供了可复现的研究平台。