A FRAMEWORK FOR SUSTAINABLE ARTIFICIAL INTELLIGENCE: LIFECYCLE ASSESSMENT AND GOVERNANCE

The rapid expansion of artificial intelligence technologies has raised significant environmental concerns, with 
projections indicating substantial increases in energy consumption, carbon emissions, and water usage through 
2030. Despite growing awareness, the field lacks a unified approach to defining, measuring, and governing the 
environmental impacts of AI systems. Current frameworks suffer from fragmented metrics, incomplete lifecycle 
coverage, and insufficient integration with policy mechanisms. This paper addresses these gaps by proposing a 
comprehensive theoretical framework for sustainable AI that integrates environmental assessment across five 
distinct lifecycle phases: design and planning, development and training, deployment and inference, operation 
and maintenance, and decommissioning. Drawing on Life Cycle Assessment theory, systems theory, and 
stakeholder perspectives, the framework provides standardized metrics for energy, carbon, water, and 
embodied impacts while incorporating governance mechanisms at each phase. The framework offers actionable 
guidance for researchers in designing sustainability-aware studies, practitioners in implementing green AI 
solutions, organizations in strategic planning, and policymakers in developing effective regulations. By bridging 
technical implementation with policy governance, this work contributes to the advancement of sustainable AI 
scholarship and provides a foundation for future empirical validation and sector-specific adaptations.