The rapid global expansion of data center infrastructure has prompted substantial clean technology research on energy, water, and carbon impacts, while the acoustic health dimension of these facilities remains virtually unstudied. Existing occupational and environmental noise assessments rely on A-weighted (dBA) metrics, which apply more than 26 decibels (dB) of attenuation at 63 hertz (Hz) and exceed 50 dB at infrasound frequencies, sharply discounting their sensitivity to infrasound and low-frequency noise (ILFN) generated by data center cooling fans, heating, ventilation, and air conditioning (HVAC) systems, backup generators, and power transformers. This narrative review synthesizes evidence from established ILFN health research alongside the emerging data center acoustics literature, identifying a consequential gap: no published study has measured the ILFN spectrum of an operational data center, nor examined health outcomes in workers or surrounding communities with respect to sub-audible acoustic exposure. Evidence from wind turbine, industrial, and laboratory contexts documents non-auditory ILFN pathways, including sleep disturbance, cardiovascular stress responses, cognitive impairment, and audiovestibular symptoms—effects that operate below the auditory threshold and are substantially undercounted by standard dBA monitoring. A prioritized research agenda is proposed, beginning with G-weighted and flat-response ILFN characterization of operational data centers across at least 1–200 Hz—a prerequisite for evidence-based acoustic design standards and health-protective infrastructure development consistent with clean technology principles.

Artificial intelligence presents a critical paradox for clean technology: while enabling unprecedented environmental optimization, AI deployment demands massive resource inputs that threaten to offset benefits. As global AI infrastructure investment approaches $500 billion annually, data center electricity consumption is projected to exceed 1000 TWh by 2030. We conducted a systematic literature review of 73 peer-reviewed empirical studies (2021–2025) to develop an Environmental Asset-Cost Framework categorizing AI’s impacts across five asset categories (energy optimization, production enhancement, green innovation, resource conservation, precision applications) and five cost categories (energy consumption, water use, e-waste, infrastructure, supply chain extraction). Our analysis reveals three critical insights: First, AI’s environmental impact follows a synthesized S-curve heuristic—a pattern derived from convergent but methodologically diverse evidence strands—characterized by initial emission reductions (0–2 years), mid-term rebound effects (2–5 years), and conditionally projected long-term optimization (5+ years). Second, geographical context creates 10–60× variation in outcomes; regions with high renewable electricity and water abundance achieve net benefits within 2–3 years, while fossil fuel-heavy, water-stressed regions may never reach net positive outcomes. Third, the rebound effect is predictable and manageable through strategic interventions. Our framework provides actionable deployment guidance, demonstrating that achieving AI’s net environmental benefits requires renewable energy infrastructure development before AI deployment, alternative cooling technologies, and policy frameworks incorporating temporal dynamics.