Leveraging Digital Twins For Ecosystem Restoration And Biodiversity Conservation In The Green Economy Framework

Abstract

Digital twin (DT) technology is emerging as a pivotal tool for addressing critical environmental challenges, especially in the fields of ecosystem restoration and biodiversity conservation, within the broader framework of the green economy. Digital twins - sophisticated, data-driven virtual replicas of physical systems - offer a transformative approach by enabling real-time monitoring, predictive modelling, and scenario-based planning. These capabilities allow for a nuanced understanding of complex ecological processes and the impacts of human activities on natural systems. The integration of DTs into ecological management introduces advanced tools for enhancing decision-making processes, improving predictive accuracy, and supporting sustainable practices that align with green economy objectives, such as carbon reduction, biodiversity protection, and efficient natural resource use. This paper employs qualitative methodologies to explore how DT technology can model species-specific behaviors and environmental conditions. This thesis focuses on integrating digital twin technology into environmental management practices, specifically for ecosystem restoration and biodiversity conservation. It explores how digital twins can model ecological changes, simulate the impact of various conservation strategies, and improve decision-making processes. Eventually, the paper examines how digital twin technology can be leveraged to support ecosystem restoration and biodiversity conservation while advancing a green economy. It also aims to demonstrate how this technology can help mitigate environmental costs associated with economic growth, in line with the principles of the Environmental Kuznets Curve (EKC), by enabling more informed and effective conservation strategies.