Paper: arXiv 2509.18633

Authors: Yara Mohajerani

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces an innovative, open-source spatial agent-based framework for modeling climate risk propagation in supply chains with endogenous adaptation mechanisms. While the mathematical formulations are accessible heuristic rules rather than complex proofs, the framework is exceptionally well-documented, highly clear, and emphasizes software reproducibility.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Model cascading climate risk & adaptation<br>in spatial supply chains"] --> B["Data Inputs:<br>Geospatial flood hazards, firm/household locations,<br>& economic network data"]
    
    B --> C["Integrated Agent-Based Model:<br>Simulates direct asset losses & indirect network disruptions"]
    
    C --> D["Endogenous Firm Adaptation"]
    
    D --> E["1. Capital Hardening<br>(Reduces direct damage)"]
    D --> F["2. Backup-Supplier Search<br>(Mitigates input disruptions)"]
    
    E --> G["Key Outcomes:<br>• Capital hardening cuts direct losses by 26%<br>• Backup search cuts supply disruption by 48%<br>• Non-flooded firms bear major indirect losses"]
    F --> G