Pollution

Mitigation strategy

1. Prioritize Algorithmic and Operational Efficiency: Implement mandatory energy-efficient model optimization techniques (e.g., model pruning, quantization, knowledge distillation) combined with carbon-aware scheduling to execute AI training and inference workloads during periods of high renewable energy availability, thereby directly reducing the operational carbon footprint and associated air pollution. 2. Establish Standardized Life Cycle Assessment and Disclosure: Develop and enforce standardized, transparent protocols, such as Life Cycle Assessment (LCA) frameworks, for quantifying and publicly reporting the full environmental impact of AI systems, encompassing embodied emissions from hardware manufacturing, operational energy and water consumption, and end-of-life electronic waste generation. 3. Regulate Resource Circularity and Waste Management: Introduce and strictly enforce regulations that require adherence to circular economy principles within the AI supply chain, focusing on maximizing the reuse and recycling of critical hardware components (e.g., GPUs, servers), and minimizing water consumption for data center cooling to reduce strain on local water resources and mitigate chemical contamination risks.