ARTIFICIAL INTELLIGENCE APPLICATIONS IN SUSTAINABLE DEVELOPMENT SYSTEMS

Abstract

Artificial intelligence has increasingly been positioned as a transformative mechanism within sustainable development systems, driven by its capacity to process complex datasets and support decision-making across environmental, economic, and social domains. Despite expanding applications, existing scholarship has inadequately addressed the interplay between technological deployment and regulatory coherence, particularly in comparative and governance-oriented contexts. This study examined the role of artificial intelligence in advancing sustainability objectives through a qualitative doctrinal and comparative research design grounded in interpretivist analysis. Primary legal materials and secondary scholarly sources were systematically reviewed, while comparative evaluation across jurisdictions enabled contextual assessment of adoption patterns and institutional responses. The findings revealed that artificial intelligence applications were significantly influenced by disparities in data infrastructure, regulatory fragmentation, and institutional capacity, limiting their effectiveness and scalability across regions. Variability in data availability and reporting standards further constrained predictive accuracy and policy reliability, while developed jurisdictions demonstrated greater capacity for optimization-driven applications compared to structurally constrained regions. The study contributed to scholarly discourse by situating artificial intelligence within governance frameworks, emphasizing the necessity of aligning technological innovation with legal norms, ethical principles, and standardized data practices. The analysis established that sustainable integration of artificial intelligence requires coordinated regulatory structures and context-sensitive implementation, reinforcing its role as both a technical and institutional instrument in advancing global sustainability objectives.