Beyond Efficiency: A Systematic Review of Energy Consumption and Carbon Footprint Across the AI Lifecycle

The rapid expansion of artificial intelligence (AI) systems has intensified concerns regarding their energy consumption and carbon footprint, raising questions about whether efficiency-focused strategies under the Green AI paradigm are sufficient to ensure system-level environmental sustainability. This study systematically synthesizes empirical evidence on the energy use and carbon emissions of AI systems across their life cycle and develops a conceptual framework to integrate sustainability constraints into AI deployment. A systematic review was conducted in accordance with PRISMA 2020 guidelines and AMSTAR-2 standards, with searches performed in Web of Science, Pubmed and Scopus up to 19 December 2025. Eligible studies quantitatively assessed energy consumption, carbon footprint, greenhouse-gas emissions, or life-cycle impacts associated with AI systems, including training, inference, hardware, and deployment infrastructures. Ten studies met the inclusion criteria. The results show that AI-related environmental impacts are substantial and highly context-dependent, with inference-phase energy demand often matching or exceeding training-related consumption in large-scale deployments. Life-cycle assessments indicate that hardware-related emissions and electricity mix strongly influence total carbon footprints, while efficiency gains are frequently constrained by system-level feedback. These findings suggest that isolated efficiency improvements are insufficient and that sustainable AI requires coordinated, system-level governance embedding energy and carbon constraints into design and operational decision-making.