Digital twin technology for environmental management: Applications, benefits, and implementation challenges

Global environmental crises, climate change, biodiversity loss, and resource depletion, demand innovative management solutions that move beyond traditional reactive approaches. Digital twin (DT) technology, which creates dynamic virtual replicas of physical systems through real-time data integration, offers transformative potential for environmental governance. Originally developed for manufacturing and aerospace, DT applications in environmental management remain underexplored despite their promising capabilities. This narrative review critically evaluates current literature on DT applications across environmental domains, examining their benefits, implementation challenges, and future directions. The review adopts an explicitly evaluative approach, distinguishing between demonstrated outcomes supported by empirical evidence and projected or modelled benefits, to provide a realistic appraisal of current technology readiness. A novel four-level maturity framework: Descriptive, Predictive, Prescriptive, and Cognitive/Autonomous, is introduced to classify DT capabilities systematically, and is explicitly differentiated from existing industrial DT maturity models by incorporating governance readiness, spatial scale compatibility, and ecological complexity as distinguishing criteria. The review analysed peer-reviewed articles, conference proceedings, and technical reports from 2015 to 2025 using multiple academic databases. Findings reveal significant DT potential across six environmental domains: climate resilience, water resource management, pollution control, energy systems, biodiversity conservation, and urban environmental planning. Most current implementations operate at Descriptive (real-time monitoring) or Predictive (scenario analysis) maturity levels, whilst advanced Prescriptive and Cognitive applications remain nascent. Key challenges include data interoperability, computational demands, scalability limitations, and governance concerns; specific technical standards such as the Open Geospatial Consortium SensorThings API are identified as critical enablers for addressing interoperability gaps. Digital twins represent a paradigm shift towards proactive, data-driven environmental management. Realising their full potential requires addressing technical barriers, establishing robust governance frameworks, and fostering interdisciplinary collaboration. Integration with artificial intelligence (AI), Internet of Things (IoT), and cloud computing will be essential for advancing DT maturity and achieving sustainable environmental outcomes aligned with United Nations Sustainable Development Goals.