Department
Civil and Environmental Engineering
Additional Department
Construction Management
Document Type
Article
Publication Date
7-24-2026
Embargo Period
9-28-2026
Abstract
The explosive growth of artificial intelligence workloads has triggered unprecedented data center construction, yet environmental assessment remains focused on operational energy efficiency. This paper introduces DC-WLC3O, a framework that simultaneously optimizes three objectives across the full data center lifecycle: whole-life cost, whole-life carbon, and material circularity index. The framework integrates cradle-to-cradle lifecycle assessment with dynamic material flow analysis, employing a four-layer analytical stack: Monte Carlo simulation with Latin Hypercube Sampling and Iman–Conover rank correlation; NSGA-III tri-objective Pareto optimization; Sobol’ variance-based global sensitivity analysis; and Many-Objective Robust Decision Making (MORDM). The framework evaluates 192 design configurations for a 30 MW hyperscale facility under three carbon pricing scenarios. Results reveal a carbon rebound paradox: operational carbon dominates grid-dependent configurations (96.2%), but low-carbon energy produces an 8.5× inversion (Welch’s t = 22.2, p < 10−39). Sobol’ analysis confirms energy strategy dominates carbon outcomes (S1 = 0.87). MORDM identifies on-site renewable energy with a full circular economy strategy as unconditionally robust (R = 0.056, stable across 94.2% of weight permutations). The cost–circularity correlation is negative (rho = −0.488, p < 10−12), overturning the assumption that circular strategies increase lifecycle costs. A post hoc stress test confirms this finding is robust to moderate perturbations in renewable LCOE (+71%), with stability declining to ≈80% under worst-case capital cost assumptions and to ≈61% when generation degradation and battery replacement are jointly considered.
Journal Title
Buildings
Journal ISSN
2075-5309
Volume
16
Issue
15
Digital Object Identifier (DOI)
https://doi.org/10.3390/buildings16152961
Comments
This article received funding through Kennesaw State University's Faculty Open Access Publishing Fund, supported by the KSU Libraries and KSU Office of Research.