Presentation Information
[X5rLbKi3-02]Evidence, Impact, and Investment: Scientometric Insights to Guide Digital Twin Priorities in Offshore Wind Development
*Madhulika Bhati1 (1. Dalhousie University (Canada))
Offshore wind development is expanding rapidly worldwide, offering substantial benefits in clean energy and climate mitigation. However, its growth introduces environmental and operational challenges that require strategic attention. Research from multiple jurisdictions has documented impacts such as chemical releases, electromagnetic fields (EMF), underwater noise, and disturbances to benthic habitats and species. Recognizing which impacts are significant helps decision-makers prioritize areas for digital twin solutions, especially when some effects may be temporary or localized.
Digital twin technology is increasingly recognized as a promising tool for addressing environmental challenges like underwater noise and habitat disturbance by enabling real-time monitoring, predictive modeling, and scenario testing. However, digital twins remain capital-intensive, requiring significant investment in data infrastructure, sensors, modeling capabilities, and long-term maintenance. Identifying which environmental or operational challenges-such as noise pollution or habitat loss-should be prioritized for digital-twin-based solutions is essential, especially in early pilot deployments.
This study provides a high-level yet comprehensive overview of the evidence on environmental stressors associated with offshore wind farms. As one of the first studies to integrate both academic and grey literature such as PhD theses, industry reports, and policy documents, it maps the global research landscape by identifying leading countries, institutions, and authors in this field. Through thematic analysis, it offers concrete insights into how offshore wind development affects the marine environment. By synthesizing global findings, the study examines whether reported environmental effects are supported by evidence. This distinction is crucial: if certain impacts are shown to be minimal or short-term, digital twin development may not need to prioritize those areas. Conversely, if strong causal relationships or persistent effects are identified, they become strong candidates for digital-twin-enabled monitoring and mitigation. Clarifying which environmental risks are well supported and which remain uncertain helps build trust and guides strategic, evidence-based decisions for industry, researchers, and policymakers.
Digital twin technology is increasingly recognized as a promising tool for addressing environmental challenges like underwater noise and habitat disturbance by enabling real-time monitoring, predictive modeling, and scenario testing. However, digital twins remain capital-intensive, requiring significant investment in data infrastructure, sensors, modeling capabilities, and long-term maintenance. Identifying which environmental or operational challenges-such as noise pollution or habitat loss-should be prioritized for digital-twin-based solutions is essential, especially in early pilot deployments.
This study provides a high-level yet comprehensive overview of the evidence on environmental stressors associated with offshore wind farms. As one of the first studies to integrate both academic and grey literature such as PhD theses, industry reports, and policy documents, it maps the global research landscape by identifying leading countries, institutions, and authors in this field. Through thematic analysis, it offers concrete insights into how offshore wind development affects the marine environment. By synthesizing global findings, the study examines whether reported environmental effects are supported by evidence. This distinction is crucial: if certain impacts are shown to be minimal or short-term, digital twin development may not need to prioritize those areas. Conversely, if strong causal relationships or persistent effects are identified, they become strong candidates for digital-twin-enabled monitoring and mitigation. Clarifying which environmental risks are well supported and which remain uncertain helps build trust and guides strategic, evidence-based decisions for industry, researchers, and policymakers.
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