Environmental Pressures and Modelled Health Pathways of Data Centers in the AI Era: A Systematic Review and Evidence Map

Data centers are a critical component of digital infrastructure, and their expansion in the artificial intelligence era raises concerns about environmental pressures and potential implications for human health. This systematic review and evidence map synthesized evidence on data-center-related environmental pressures, exposures and health impacts. Bibliographic databases, Google Scholar, preprint servers, gray literature and citation searching were used to identify eligible studies. Methodological quality was assessed using QualSyst or the JBI checklist, and each result was classified along a four-level pathway from environmental pressure to population exposure, modeled health impact and observed health outcome (PROSPERO CRD420261502966). Thirty-six studies yielding 112 quantitative estimates were included. The most downstream level reached was environmental pressure only in 18 studies (50.0%), population exposure in 12 (33.3%) and modeled health impact in only six (16.7%); no study reached the observed-health-outcome level. Across heterogeneous satellite, modeling, and field-monitoring studies, reported localized temperature differences ranged from 0.35 to 2.07 °C, with preliminary evidence of vegetation loss. Exposure assessment was predominantly model-based (26/36), and all six studies reaching the health-impact level estimated rather than observed health outcomes; four of these six studies were preprints. Environmental pressures attributable to data-center activity have been quantified across multiple domains, but evidence linking these pressures to human health remains limited to modeled health impacts. No included study evaluated observed human-health outcomes using epidemiological or clinical data. Therefore, the current evidence base does not establish whether data-center-related exposures are associated with health effects in exposed populations.