Ecosystem and Environment

Mitigation strategy

1. Prioritize and mandate the use of fossil-free energy sources for AI infrastructure by requiring data centers to be powered by 100 percent *new and additional* renewable energy, avoiding the diversion of existing clean power from other sectors. Additionally, implement robust siting regulations to prohibit the construction of data centers in water-scarce or ecologically sensitive areas to prevent the depletion of local resources. 2. Require comprehensive optimization of AI model training and inference by accelerating the adoption of algorithmic efficiency techniques, such as model pruning, quantization, knowledge distillation, and the deployment of Small Language Models (SLMs) over larger models when appropriate. This must be coupled with the widespread utilization of energy-efficient, purpose-built AI hardware (e.g., optimized GPUs and TPUs) and carbon-aware workload scheduling to minimize overall energy and associated carbon consumption per compute cycle. 3. Establish a global regulatory framework that mandates standardized, transparent reporting and public disclosure of the full environmental impact lifecycle of AI systems, encompassing operational carbon emissions, embodied carbon from hardware and infrastructure construction, and local water consumption metrics. This framework should be supported by independent, third-party auditing to ensure accountability and to provide accurate data for policymakers and researchers.