From economic activity to material constraints: linking macroeconomic models with dynamic stock-flow analysis

Macroeconomic and integrated assessment models are widely used to evaluate trade, climate and industrial policies, but they are generally not designed to ensure the physical consistency of material flows. This limitation is particularly relevant for low-carbon transitions, where technology deployment depends on critical raw materials, globally fragmented supply chains, recycling potentials and end-of-life dynamics. Even when input-output and CGE databases are enhanced with additional sectors for critical raw materials and clean technology manufacturing, sectoral detail alone is insufficient to represent technology stocks, material balances, capacity vintages, recycling flows or mineral supply constraints.

This paper presents ongoing work from the ENTICE project on linking macroeconomic models with dynamic physical stock-flow models. The objective is to develop a flexible model-linking framework that can translate outputs from macroeconomic and IAM-CGE models, such as sectoral activity, final demand, trade flows, technology deployment and energy-service demand, into physical indicators of material demand, extraction, refining, recycling and supply-chain exposure. The approach builds on DYNERIO, a Dynamic Extraction and Recycling Input-Output framework designed to assess critical material demand in global energy transition scenarios.

The proposed framework focuses on refined physical coupling rather than static material multipliers. Macroeconomic model outputs are passed through a harmonisation layer that maps economic sectors to technologies, materials and regions. A dynamic physical module then represents technology stock evolution, annual capacity additions and retirements, material intensities, end-of-life flows, recycling potentials and primary material requirements. The framework is illustrated through an application to copper in low-carbon electrification, showing how macroeconomic decarbonisation scenarios can be translated into indicators of material demand, recycling availability, primary extraction needs and trade-related exposure. The paper contributes to more physically grounded IAM-based assessment of critical materials, recycling potentials and supply-chain risks in low-carbon transitions.