Environmental Digital Twins: a review of challenges and opportunities

Abstract

Digital Twin (DT) technology has emerged as a transformative paradigm for environmental monitoring, modelling, and sustainability governance, yet its development across ecological and territorial domains remains fragmented and unevenly documented in the literature. This review provides a systematic mapping of Ecological Digital Twin (EcoDT) and Environmental Digital Twin applications published between 2020 and 2025, analysing a corpus of 121 peer-reviewed studies spanning urban environments, agriculture, marine and coastal systems, river and lake networks, forestry, ecology, glaciers, Earth system science, and building sustainability. Drawing on a structured analytical matrix , the review focuses on Technology Readiness Level (TRL), remote sensing integration, data-related challenges, and future development priorities, interrogating Dts technological maturity, sustainability framing, and data readiness in practice. Results reveal a field in active but early-stage development: the TRL distribution is concentrated between levels 2 and 6, with no study in the corpus reaching deployment-ready status, and sustainability framing remains predominantly environmental in orientation, with social and economic co-benefits systematically underrepresented across nearly all application themes. Integration, interoperability, and calibration emerge as the most pervasive and structurally recurring technical constraints. Scaling and deployment, alongside governance and institutional alignment, dominate the stated future priorities of reviewed studies. The review concludes by advocating for the adoption of F.A.I.R., C.A.R.E., and T.R.U.S.T. data governance principles as a foundational framework for advancing EcoDTs and Environmental DTs towards integrated, equitable, and policy-relevant implementation at territorial and ecosystem scales.

Keywords: digital twin, ecoDTs, environmental digital twin, sustainability, environmental monitoring, what-if-scenarios, ecology data, EO

DOI: 10.54941/ahfe1007273