Environmental harms

Mitigation strategy

1. Implement a mandate for Sustainable AI by prioritizing operational efficiency and transparency across the compute infrastructure life cycle. This includes requiring the use of renewable energy sources and advanced cooling technologies (e.g., liquid cooling) for data centers, alongside the adoption of resource-efficient algorithmic techniques such as small language models (SLMs) and optimized inference to minimize the energy and water footprint per computational task. 2. Establish stringent due diligence and accountability standards for the supply chain of AI-enabling hardware, from critical mineral extraction (the 'cradle' phase) to end-of-life management (the 'grave' phase). This requires enforcing high rates of material recovery and recycling through Extended Producer Responsibility schemes to mitigate resource depletion and the proliferation of toxic electronic waste (e-waste). 3. Develop and enforce explicit, measurable environmental governance frameworks for AI deployment, utilizing Life Cycle Assessment (LCA) methodologies to track total environmental costs. The framework must actively regulate against "environmental problem shifting" and unintended higher-order effects, such as AI's application in optimizing environmentally harmful sectors (e.g., fossil fuel exploration) or promoting unsustainable consumption patterns, thereby ensuring a net-positive environmental impact.