Sustainable Materials for Reducing the Environmental Footprint of Artificial Intelligence Data Centers in the United States

Abstract

Artificial intelligence (AI) is expanding data-centre infrastructure in the United States, increasing demand for electricity, cooling water, semiconductor materials and electronic equipment. This review examines how sustainable materials and integrated engineering strategies may reduce environmental pressures across the AI data-centre life cycle. It considers operational energy demand, greenhouse-gas emissions, direct and indirect water consumption, critical-material requirements, thermal management, renewable-energy integration, storage technologies and end-of-life equipment management. High-thermal-conductivity materials, electrically insulating ceramics, thermal-interface composites and phase-change materials can support heat transfer and temperature control. Direct-to-chip liquid cooling and immersion cooling offer additional options for high-density computing, although material compatibility, leakage, toxicity, durability and recyclability require careful assessment. Renewable electricity can be complemented by supercapacitors, lithium-based batteries, redox-flow systems, hydrogen and thermal storage, with technology selection guided by the timescale of power demand. Measures such as modular design, longer server service life, repair, component reuse and recovery of critical materials may reduce embodied impacts and electronic waste. The review emphasises that improvements in one environmental metric can increase burdens elsewhere, particularly across the energy–water–carbon nexus. Accordingly, the assessment of sustainable AI infrastructure requires a life-cycle perspective, transparent facility-level reporting, responsible material sourcing and coordination between materials engineering, electricity supply, cooling design and circular management. No individual material or technology is sufficient to address these interdependent impacts.