Environmental

Mitigation strategy

1. Mandate a rapid, verifiable transition to **100% new and additional renewable energy sources** for all AI infrastructure, complemented by the implementation of **carbon-aware scheduling and grid-aware computing**. This systemic measure directly addresses operational carbon emissions by prioritizing clean power and shifting high-intensity computational workloads to periods of low energy demand and high renewable availability. 2. Establish **mandatory model efficiency standards** across the entire AI lifecycle, focusing on techniques such as **algorithmic optimization, model pruning, knowledge distillation, and hardware-algorithm co-design**. This will significantly reduce the required computational resources (energy and memory) for both model training and post-deployment inference, thus minimizing the overall environmental footprint. 3. Implement a **comprehensive resource stewardship framework for AI hardware and data centers**, which includes three key pillars: 1) **prohibiting planned obsolescence** and championing the right to repair to extend hardware lifespan; 2) **aggressively minimizing water consumption** through advanced cooling techniques and water recycling; and 3) adopting materials with **low-embodied carbon** for infrastructure construction to address non-operational emissions.