Environmental damage

Mitigation strategy

1. Implement algorithmic and architectural optimization to minimize computational load and energy consumption across the AI lifecycle. This encompasses adopting **Green-in-AI** techniques such as **model pruning, knowledge distillation, and transfer learning**, and prioritizing the development and deployment of **smaller, energy-efficient models** (SLMs) over larger, resource-intensive ones to directly reduce the power demand for both training and inference. 2. Mandate and accelerate the transition of AI computing infrastructure (data centers) to **100% carbon-free and renewable energy sources**. This includes implementing **strategic siting** policies that prioritize regions with high grid decarbonization rates, low water stress, and favorable climates for advanced, water-efficient cooling techniques to minimize the aggregate carbon and water footprint of AI operations. 3. Establish **global standardized metrics and mandatory reporting frameworks** for quantifying and disclosing the complete environmental lifecycle impact of AI systems, including energy consumption, water usage, and associated greenhouse gas emissions. This enhanced transparency is essential for enabling accountability, benchmarking progress, and informing targeted policy interventions to govern AI's environmental fallout.