AI's Thirst for Rare Earths: Unearthing New Environmental Concerns
Jürgen Riedel
Innovation Coach helping businesses develop modern strategies using a multi-disciplined approach anchored in GenAI solutions to create trusted businesses | Founder@Baiomics, a DeepTech, ML/AI Tech Consultancy | PhD
The mining, extraction, and refining rare earth elements (REEs) significantly impact the environment. This impact varies across countries, primarily due to differences in mining practices, environmental regulations, and geological conditions.
China dominates the global REE market due to lax environmental regulations and high-pollution methods. The Bayan-Obo mine, the world's largest REE mine, is infamous for its environmental impact, with over 70,000 tons of radioactive thorium in its tailing pond, threatening drinking water sources. Despite efforts to address these issues, the environmental damage is significant and ongoing [1] .
Australia, the next largest REE producer after China, faces similar challenges. The co-extraction of thorium and uranium with the metals generates large volumes of toxic and radioactive materials. However, the Australian mining industry is adhering to stricter environmental standards. The Public Health Association of Australia recommends sourcing rare earths through recycling electronic waste and developing strict environmental guidelines for rare-earth projects [2].
In the United States, rare earth mining poses environmental challenges, including contamination and ecosystem disruption. Mining processes generate wastewater ponds filled with acids, heavy metals, and radioactive material, potentially leaking into groundwater. However, efforts are underway to reduce mining's environmental footprint [3] .
Mining methods release toxic chemicals into the environment, resulting in contaminated water supplies and large amounts of toxic waste. For every ton of rare earth produced, mining yields significant amounts of dust, waste gas, wastewater, and radioactive residue [4].
REE extraction and processing are complex and environmentally hazardous. Processing requires large amounts of water and chemicals, leading to significant waste [5]. Pollution and radioactive waste are common, with wastewater ponds containing acids, heavy metals, and radioactive material. The extraction process is slow and imperfect, leading to high waste production [5] . Recycling is suggested as a potential solution to these environmental concerns [5].
Mining operations use large amounts of water and can pollute local freshwater supplies. Mining tailings, often radioactive or toxic, are a significant source of environmental pollution. Mining leads to land use changes, impacting local ecological systems, and contributes to greenhouse gas emissions [6].
Rare Earth Element (REE) Mining - Specific Insights
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1. Acid Mine Drainage and Waste Products: AMD, involving highly acidic water rich in heavy metals, is a critical issue in REE mining. It can severely degrade soils, water reservoirs, and rivers [7].
2. Environmental and Health Impacts at Specific Sites: The Bayan Obo Ore Deposit in Inner Mongolia exemplifies the severe environmental consequences of mismanaged mining waste [8] .
3. Waste Generation Metrics: Mining for rare earths generates about 2,000 tons of toxic waste for every ton of rare earth extracted [8].
4. Water Usage and Impact: Global mining operations used an estimated 1.6 billion cubic meters of water in 2006, exacerbating water scarcity and pollution in water-stressed regions [9].
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It is undeniable that REE mining is necessary for various applications, but it poses significant environmental and societal challenges. Managing these impacts is crucial for sustainable development.
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References
Intern at Scry AI
7 个月Rightly said. While AI holds promise in addressing climate change, concerns arise over its environmental impact. The massive demand for rare minerals in electronic systems, including IoT devices and computers, poses recycling challenges, leading to potential environmental hazards. Additionally, the substantial electricity consumption of AI systems and data centers, expected to reach 8% of global power demand by 2030, raises apprehensions about increased fossil fuel usage. Deep Learning Networks (DLNs), particularly Transformers, contribute significantly to this electricity consumption, with computations for deep learning research causing a 300,000 times increase in electricity use from 2012-2018. The environmental costs of training DLNs, including carbon emissions equivalent to five times the lifecycle emissions of an American car, raise concerns about sustainability. Critics argue that achieving marginal improvements in model performance may result in outrageous computational, economic, and environmental costs, emphasizing the need for breakthroughs to balance environmental impact and AI advancements. More about this topic: https://lnkd.in/gPjFMgy7
Senior Recruitment Professional working across IT and Change Management in Regulated Industries, including Financial Services, Banking, RegTech, FinTech and ESG initiatives
11 个月Really liking your articles Jurgen