Mining is a fundamental industry for the modern world, providing essential materials like iron, copper, cobalt, and lithium. However, there’s a significant gap in global knowledge about the mining sector, particularly in terms of its environmental and community impacts.
The industry’s adverse effects are notable. In Indonesia, the world’s largest coal exporter, rainforests are being cleared for coal mines, leading to environmental degradation and safety hazards. Similarly, the increasing demand for lithium, vital for electric vehicle batteries, has sparked environmental concerns, as seen in Serbia where protests halted a proposed lithium mine.
The sector is plagued by substantial data gaps. There’s no comprehensive inventory of mine sites, and data on production, waste, pollution, and resource consumption is often unreliable or unavailable. This is particularly true for illegal mining operations, like a large portion of gold mining in Colombia and Venezuela. These gaps in data hinder the ability to fully understand the industry, track decarbonization efforts, and inform policymaking.
Addressing these challenges involves several key strategies. First, researchers must acknowledge the limitations and biases of existing data, which predominantly rely on company reports and databases like S&P Capital IQ Pro. Many mines are not adequately represented in these sources due to reasons ranging from illegal activities to poor reporting. It’s essential for research to include acknowledgments of data bias and completeness.
Second, there is a need for better coordination and data sharing among researchers. Most mining impact studies are local, lacking a global perspective. Adopting data sharing platforms and adhering to FAIR (findability, accessibility, interoperability, and reusability) principles are crucial steps to make data more accessible and usable.
Third, the lack of transparency in the sector must be addressed. This involves identifying the root causes of information gaps, often due to a lack of historical accountability, confidentiality issues, and other complexities. Improving the transparency and availability of mining data is particularly important for small, artisanal, and illegal mining operations.
Lastly, supplementing reported data with remote sensing techniques and artificial intelligence can fill in many data gaps. This approach is particularly useful for historic or abandoned mines. However, it faces challenges like the complexity of mining operations and the need for extensive, expert-labelled datasets to train AI models effectively.
Significant research and data collection investments are crucial, especially as the demand for minerals increases with the growth of clean technologies. Having accurate and comprehensive data is vital to assess the impacts and risks of expanding mining activities.