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Modeling Supply-Chain and Transport Resilience to Climate Risks: Lessons from Four Regions

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Listed:
  • Celian Colon
  • Stephane Hallegatte

Abstract

Building resilience to natural hazards requires more than identifying critical assets. It requires understanding how supply chains respond to disruptions: where suppliers are located, what inventories firms hold, how diversified their sourcing is, and how products are transported. This paper summarizes key insights from DisruptSC, a spatial agent-based model that jointly represents the transport network and firm-level supply chains, applied to four countries or regions: Tanzania, Cambodia, Ecuador, and the Middle Corridor (Central Asia and South Caucasus). Five policy-relevant findings emerge. First, the duration of a disruption is a primary driver of total economic losses, making fast recovery a crucial lever for risk reduction. Second, the distribution of direct damages across firms and facilities matters as much as the total damages in determining total economic losses. Third, compounding events striking in close succession amplify losses in non-trivial ways, so that individual events cannot be assessed in isolation. Fourth, shorter supply chains do not always increase resilience: they buffer small, frequent disruptions but amplify large ones, reflecting a trade-off between efficiency and resilience that depends on the characteristics of the firm network and the nature of the risks. And fifth, resilience is a network externality—its benefits spill across firms and across borders — so markets under-provide it, and coordinated policy, within and between countries, has a role to play. The paper also illustrates how the model can be used for operational risk assessments or investment prioritization through transport investment stress testing, hotspot identification, and cost-benefit analyses that capture supply chain impacts.

Suggested Citation

  • Celian Colon & Stephane Hallegatte, 2026. "Modeling Supply-Chain and Transport Resilience to Climate Risks: Lessons from Four Regions," CESifo Working Paper Series 12950, CESifo.
  • Handle: RePEc:ces:ceswps:_12950
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    References listed on IDEAS

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    1. Angel Aguiar & Maksym Chepeliev & Erwin L. Corong & Robert McDougall & Dominique van der Mensbrugghe, 2019. "The GTAP Data Base: Version 10," Journal of Global Economic Analysis, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, vol. 4(1), pages 1-27, June.
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    Keywords

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    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • D57 - Microeconomics - - General Equilibrium and Disequilibrium - - - Input-Output Tables and Analysis
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures

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