Silicon carbide (SiC) is a niche nonmetallic material that is essential in many industrial processes. Here, we integrate material flow analysis and supply chain resilience analysis to understand global SiC stocks and flows and to assess its supply chain. We use industry interviews to fill data gaps and collect information on the SiC system to overcome data scarcity. We find that globally around 1000 kt of SiC is produced each year. The biggest use of SiC is the abrasives industry (40%), followed by metallurgy (28%), refractories (20%), technical ceramics (0.7%), other uses (0.7%), and semiconductors (0.01%). As an energy-intensive material, the SiC supply chain is under pressure, increasing the relevance of resilience considerations. Besides typical supply chain risks such as low diversity of supply and geopolitical trade restrictions, SiC particularly faces risks due to its energy-intensive production process and associated emissions. In the SiC semiconductor supply chain, losses of nearly 75% are a particular issue. Due to high demand in the SiC market, stockpiles are negligible, and substitution is difficult in most sectors. We find that in the case of SiC, sustainability measures such as use reduction, recycling, or decreasing energy use or emissions would also positively contribute to supply chain resilience. This article met the requirements for a gold-gold JIE data openness badge described at http://jie.click/badges.

Re-use and recycling in the construction sector is essential to keep resource use in check. Data availability about the material contents of buildings is significant challenge for planning future re-use potentials. Compiling material intensity (MI) data is time and resource intensive. Often studies end up with only a handful of datapoints. In order to adequately cover the diversity of buildings and materials found in cities, and accurately assess material stocks at detailed spatial scopes, many more MI datapoints are needed. In this work, we present a database on the material intensity of the Dutch building stock, containing 61 large-scale demolition projects with a total of 781 datapoints, representing more than 306,000 square meters of built floor space. This dataset is representative of the types of buildings being demolished in the Netherlands. Our data were empirically sourced in collaboration with a demolition company that explicitly focuses on re-using and recycling materials and components. The dataset includes both the structural building materials and component materials, and covers a wide range of building types, sizes, and construction years. Compared to the existing literature, this paper adds significantly more datapoints, and more detail to the different types of materials found in demolition streams. This increase in data volume is a necessary step toward enabling big data methods, such as data mining and machine learning. These methods could be used to uncover previously unrecognized patters in material stocks, or more accurately estimate material stocks in locations that have only sparse data available. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.