Environmental scientists are increasingly turning to artificial intelligence to understand complex systems, but prediction alone may not be enough. A new Perspective proposes that combining AI with environmental digital twins could create continuously updated virtual counterparts of real-world environmental systems, helping researchers move from simply observing environmental change toward predicting risks, testing interventions, and supporting adaptive decisions .
Published in Artificial Intelligence & Environment , the article outlines the concepts, architecture, applications, and remaining challenges of AI-enabled environmental digital twins. The authors examine how these systems could connect real-time observations, physical and data-driven models, AI algorithms, and decision feedback in a continuously evolving loop.
“Environmental systems are dynamic, uncertain, and often difficult or even impossible to study through conventional trial-and-error experiments,” said corresponding author Dawei Lu. “AI-enabled digital twins provide a promising way to combine observations, models, prediction, and feedback so that we can explore environmental change virtually while remaining connected to what is happening in the real world.”
A digital twin is more than a computer simulation. The Perspective stresses that a true digital twin requires a dynamic connection between a physical system and its virtual representation. A digital model has no automatic exchange with the physical world, while a digital shadow receives data in one direction. A digital twin involves recurring two-way interaction, allowing virtual predictions or decisions to inform real-world actions and the resulting responses to update the virtual system.
AI can strengthen this cycle by integrating heterogeneous observations, detecting abnormal conditions, accelerating complex simulations, forecasting future states, quantifying uncertainty, and optimizing decisions. The authors describe a five-layer framework spanning the physical environmental system, sensing and connectivity, data integration, virtual modeling, and AI-enabled prediction and feedback.
Potential applications are already emerging across energy systems, water management, and natural environmental systems . In wastewater treatment, AI-assisted digital-twin approaches can support process optimization and predictive maintenance. In urban drainage and watersheds, they can help forecast floods and evaluate management strategies. Atmospheric and coastal systems can combine sensor, satellite, and modeling data for air-quality forecasting, flood warning, and environmental risk assessment.
However, the authors caution that many systems described as digital twins are not yet fully bidirectional. Most remain digital shadows, offline models, or human-supervised decision-support systems. Three major barriers stand out: limited physical-virtual feedback, difficulty representing microscale and extreme-condition processes, and the lack of standardized architectures, benchmark datasets, and evaluation frameworks.
Future systems, the researchers argue, should continuously adapt as new data arrive, explicitly quantify uncertainty, and undergo ongoing validation rather than one-time testing. In high-risk settings such as nuclear safety, pollution emergencies, and extreme climate events, digital twins should support human experts rather than replace them.
The Perspective also points to new possibilities in environmental instrument development and predictive digital toxicology , where virtual experimentation could help optimize monitoring technologies and connect environmental exposure with biological or health effects.
Ultimately, AI-enabled environmental digital twins could provide a framework for environmental science that is more predictive, adaptive, and model-driven , allowing researchers to test possible futures before decisions are made in the physical world.
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Journal reference: Qin Y; Wu Y; Liu Y; et al. AI-enabled environmental digital twins: a new paradigm for predictive environmental research. AI Environ. 2026, 1(3): 151-165. DOI: 10.66178/aie-0026-0021
https://www.the-newpress.com/aie/article/doi/10.66178/aie-0026-0021
About the Journal:
Artificial Intelligence & Environment is an international multidisciplinary platform for communicating advances in fundamental and applied research on the intersection of environmental science and artificial intelligence (AI). It is dedicated to serving as an innovative, efficient and professional platform for researchers in the cross-discipline fields of earth and environmental sciences, big data science and AI around the world to deliver findings from this rapidly expanding field of science. It is a peer-reviewed, open-access journal that publishes critical review, original research, rapid communication, view-point, commentary and perspective papers.
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AI-enabled environmental digital twins: a new paradigm for predictive environmental research
9-Sep-2026