Progress in Machines and Systems Koodichimma Chinazom Ibe-Ariwa 15 2 2026 https://doi.org/10.6025/pms/2026/15/2/68-85 https://www.dline.info/pms/fulltext/v15n2/pmsv15n2_2.pdf The rapid expansion of artificial intelligence (AI) has drastically increased the electricity demand of hyperscale data centers, making their carbon footprint a critical sustainability concern. However, the relative contributions of geographic, infrastructural, and operational factors to these emissions remain poorly quantified. This study systematically identifies and ranks the primary drivers of carbon emissions in AIoriented hyperscale data centers. Using a large scale synthetic telemetry dataset comprising over 71,000 complete observations, we employed analysis of variance (ANOVA), bivariate correlations, and multivariate ordinary least squares (OLS) regression with heteroscedasticity robust standard errors to evaluate the independent effects of region, energy source, cooling strategy, and workload intensity. Results reveal that geographic region and primary energy source overwhelmingly dominate the carbon footprint, collectively explaining nearly 90% of the adjusted variance. Transitioning from conventional grid power to renewables reduced mean emissions by approximately 84%, while siting in the EU versus the APAC region yielded a similar magnitude of reduction. Cooling strategy (liquid versus air) and hardware architecture (GPU versus standard compute servers) emerged as significant secondary levers. Surprisingly, computational workload intensity exhibited only a marginal association with total emissions once infrastructural variables were controlled. These findings demonstrate that decarbonizing AI infrastructure requires structural interventions such as strategic geographic siting, 24/7 renewable power purchase agreements, and liquid cooling mandates rather than relying solely on computational efficiency or workload optimization.