Quantifying Demand Shocks in the Green and Digital Transition

2026-06-26

AI Summary

This study addresses the challenge of disentangling multiple demand shocks driving price volatility in critical metals, particularly the hard-to-identify structural demand shifts stemming from green and digital transitions. The authors innovatively construct monthly derived demand indices for cobalt, copper, and nickel using high-frequency internet search data and embed these into a structural vector autoregression (SVAR) framework. By imposing zero, sign, and magnitude restrictions, the model effectively isolates supply shocks, conventional demand shocks, and transition-related demand shocks. This work provides the first quantitative identification of an independent transition demand shock driven by the diffusion of metal-intensive technologies, revealing its significant and persistent impact on copper and nickel prices, in contrast to the weaker and more transitory effects of supply and other demand shocks.

Abstract

We use web search data to construct monthly indexes of derived demand for cobalt, copper, and nickel, which are key inputs in technologies driving the energy and digital transitions. We incorporate these indexes into Structural Vector Autoregressive (SVAR) models of global metal markets and identify structural shocks using zero, sign, and magnitude restrictions. This approach disentangles supply shocks from several demand-side drivers of metal prices and isolates a transition demand (TD) shock linked to the diffusion of metal-intensive technologies. We find that TD shocks generate persistent price effects, especially for copper and nickel, whereas supply and metal-specific demand shocks are more immediate and less persistent.

Problem

• demand shocks
• green transition
• digital transition
• metal markets
• structural identification

Innovation

• web search data
• Structural Vector Autoregressive (SVAR)
• transition demand shock
• derived demand index
• metal markets