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AI-enabled environmental digital twins: a new paradigm for predictive environmental research

Year: 2026

Type: article

Abstract: Environmental systems are often characterized by strong dynamics, multiscale coupling, nonlinear responses, and high uncertainty. These features limit conventional approaches based on trial-and-error experimentation and static modeling. While artificial intelligence (AI) has greatly enhanced environmental data analysis and prediction, complex environmental systems further require dynamic coupling among data, models, physical processes, and decision feedback. Digital twins dynamically connect physical systems and virtual representations through real-time data exchange, modeling, analytics, and feedback to enable dynamic understanding, prediction, and intervention. This Perspective examines the core concepts, technical architectures, and maturity levels of AI-enabled environmental digital twins, with particular attention to their conceptual boundaries and physical–virtual integration. It also highlights emerging applications across environmentally relevant energy systems, water environment management, and natural environmental systems. The reviewed studies span different levels of physical–virtual integration, from offline digital models and one-way digital shadows to a smaller number of bidirectionally coupled digital twins. We identify three critical challenges: limited bidirectional coupling, insufficient representation of microscale processes, and a lack of standardized architectures and fit-for-purpose evaluation frameworks. We further discuss emerging opportunities in environmental equipment development and predictive digital toxicology, where AI-enabled digital twins may support virtual experimentation, adaptive equipment optimization, dynamic risk assessment, and personalized environmental exposure prediction. The integration of AI with environmental digital twins may enable adaptive learning, uncertainty-aware prediction, intelligent decision-making, and predictive environmental management, thereby accelerating the transition from passive monitoring toward predictive, adaptive, and model-driven environmental science.

Source: Artificial Intelligence & Environment

DOI: 10.66178/aie-0026-0021