The global transition to low-carbon technologies hinges on secure supplies of critical minerals like cobalt, yet interconnected supply chains are increasingly vulnerable to geopolitical tensions and frequent external disruptions. Existing risk assessments often treat commodities in isolation, overlooking the upstream–downstream dependencies that amplify cascading failures. Here we show systemic risks in global cobalt flows from 1998 to 2019 across 230 countries and regions by integrating trade-linked material flow analysis with a multilayer shock propagation model. Our results reveal that disruptions propagate through alternating horizontal–vertical and direct–indirect pathways, with risk concentrating at the mining stage but accumulating predominantly in refining–manufacturing bridges. These cascades yield abrupt nonlinear failures and an avalanche network four times denser than the underlying physical supply chain. Nations with low systemic fragility but high exposure rate—such as Indonesia, South Africa, and Mexico—are particularly susceptible to common random disruptions and lack resilience or effective response. Over the past two decades, global systemic risks have followed a volatile but upward trend. These findings highlight that national mitigation strategies are necessary but insufficient; achieving resilience requires stage-aware, system-level coordination and multilateral cooperation.

Precise identification and categorization of building materials are essential for informing strategies related to embodied carbon reduction, building retrofitting, and circularity in urban environments. However, existing building material databases are typically limited to individual projects or specific geographic areas, offering only approximate assessments. Acquiring large-scale and precise material data is hindered by inadequate records and financial constraints. Here, we introduce a novel automated framework that harnesses recent advances in sensing technology and deep learning to identify roof and facade materials using remote sensing data and Google Street View imagery. The model was initially trained and validated on Odense's comprehensive dataset and then extended to characterize building materials across Danish urban landscapes, including Copenhagen, Aarhus, and Aalborg. Our approach demonstrates the model's scalability and adaptability to different geographic contexts and architectural styles, providing high-resolution insights into material distribution across diverse building types and cities. These findings are pivotal for informing sustainable urban planning, revising building codes to lower carbon emissions, and optimizing retrofitting efforts to meet contemporary standards for energy efficiency and emission reductions.