Cobalt

Global demand for cobalt has steadily increased as demand for clean energy technologies, consumer electronics, and electric vehicles has grown. Cobalt is an integral component of the two main competing electric-car battery technologies, nickel manganese cobalt and nickel cobalt aluminum batteries (Hunt 2018, IISD 2018), as well as the lithium ion batteries commonly used in consumer electronics. The Democratic Republic of the Congo has the largest deposits of cobalt in the world and produces more than 63 percent of the global supply (IISD 2018); in 2018, that share is believed to have been around 72 percent (Clowes 2019). Numbers vary, but reliable estimates suggest that a fifth of the DRC’s cobalt production (Clowes 2019) and a tenth of global production comes from ASM workers (IISD 2018).

Cobalt is often found in shallow deposits, allowing it to be mined by hand with rudimentary tools such as hammers, picks, and shovels (see Washington Post, Feb. 28, 2018). The mined ore is then crushed, washed—usually by hand in local streams—and sorted into bags for sale to traders.

Cobalt mining has negative effects on human and environmental health. People living near and working in cobalt are exposed to cobalt and other heavy metals through dust inhalation (Nkulu et al. 2018) and water and food contamination (Cheyns et al. 2014). Inhaling cobalt dust can cause hard metal lung disease, which can be fatal in humans, and cobalt contamination can cause birth defects in children. Cobalt enters water when the ore is washed in local water sources that are also used by local communities for cooking and drinking water (see Esri storymap, cobalt mining in Katanga, DRC)

Guiding questions for innovation

  • What role can consumers play in driving and ensuring environmentally responsible ASM for cobalt, tin, tantalum, tungsten, colored gemstones, gold, and other resources mined through ASM?

  • How can downstream manufacturers and brands influence the environmental and social conditions of ASM mine sites to ensure reliable and environmentally responsible sources for necessary minerals, metals, and gemstones?

  • Is it possible to know where past, present, and future ASM sites are located? In what ways can this knowledge incentivize different stakeholders along the supply chain to improve environmental and social outcomes?

  • What innovative financial tools or business models can be applied to ASM to reduce environmental and human health impacts at mining sites?

  • What tools and techniques used by large and industrial scale mining operations can be adapted for ASM to prevent or remediate negative environmental and human health impacts?

  • Where are there currently data on ASM, how can more useful data be collected? Who needs access to these data to make better decisions about environmental and social outcomes of the ASM sector?

Need some inspiration?

We realize that it isn’t always obvious to innovators outside of their fields to see the application of their technology in ASM. We’ve identified some technologies where we see ASM use-cases for existing technologies. These are suggestions and do not indicate any preference of the Challenge administrators or judges; nor is this list meant to be exhaustive. These suggestions are provided to give innovators some ideas on where there might be application for these techniques. Ideally applicants read this list, become inspired, and come up with their own ideas on how to apply their innovations to solve the ASM problems described above.

  • Field-ready, easy to use, and relatively inexpensive metal sensors using advanced techniques such as:

    • absorption and fluorescence spectroscopy;

    • hyperspectral imaging;

    • resonant frequency lidar;

    • electronic sensors using field-effect transistors; and,

    • nanotextured metal-films detecting surface plasmon resonance.

  • Earth observation techniques from satellites or drones and corresponding data analysis.

  • Mineralogy techniques such as x-ray diffraction, electron microscopy, and optical microscopy.

  • Application of AI and machine learning to convert data into better decisions and actions.