Sustainable AI Governance: A Theoretical Framework for Legal Compliance and Environmental Accountability A Systematic Conceptual Review

Abstract

The fast increase in artificial intelligence (AI) in vital points in society has created significant governance issues at the interface of environmental sustainability and legal responsibility. Although there is a rich literature on AI ethics, technical safety, and standalone regulatory compliance, a cohesive theoretical framework that encompasses both legal compliance and environmental responsibility conditions under one governance framework have not existed yet, seemingly conspicuously in the literature. The review thus poses the question of how legal compliance requirements and environmental accountability requirements for AI systems can be incorporated in one, theoretically coherent governance architecture, and what are the structural gaps in existing regulatory instruments in this context? This gap is filled in this paper, by introducing the Sustainable AI Governance Framework (SAGF) a conceptual model, three pillars in theory which integrates regulatory coherence, environmental accountability and legal compliance architecture. A systematic conceptual review of studies published in the period 2021–2025 and indexed in Scopus, WoS and PubMed (search on 3–10 March 2026) was carried out after PRISMA 2020. For empirical studies, the Mixed Methods Appraisal Tool was used to assess the quality of the studies and for doctrinal and conceptual studies, a five-item checklist was used. To develop the SAGF, 58 articles in sustainability science, environmental law, governance theory and AI regulation were selected fulfilling the inclusion criteria. The article outlines seven key global regulatory tools, seven research gaps (six corpus-derived and one framework-level), and presents a policy roadmap plan that includes mandatory carbon disclosures by AI, legal harmonization across jurisdictions, and tiered organizational standards of compliance. The results have shown that current regulatory frameworks are methodically ineffective in uniting environmental and legal aspects of AI regulation and expose structural gaps, which are inadequately addressed by technical norms and soft-law tools. The paper adds a theoretically sound and practically feasible model to help legislators, regulatory bodies, AI creators, and legal professionals negotiating the twin demands of both sustainable and legally responsible AI use on a globally scale.