Applying the Bow Tie Method to Evaluate Emerging Risk: The Case of Carbon Capture and Water Stress.

Authors

Matt J Weisner, Vincent P Paglioni, Ryan P Scott

Published in

Risk analysis : an official publication of the Society for Risk Analysis. Volume 46. Issue 7. Pages e70296.

Abstract

Emerging environmental risks are often shaped not by a lack of knowledge alone, but by fragmented information across systems, disciplines, and levels of governance. This fragmentation limits the ability of local decision-makers to identify and respond effectively to rapidly developing technologies. This paper introduces a novel bow tie risk assessment framework as a practical tool for identifying and organizing these risks. By integrating engineering, environmental, and policy perspectives, the approach captures interactions across land use, water systems, and governance structures. We apply the framework to carbon capture, utilization, and storage (CCUS) development in Colorado. Analysis shows that tracking how CCS, CCU, and CCUS are defined and applied across institutions reveals gaps in accountability, coordination, and risk identification. These inconsistencies contribute to policy drift, obscure system-level impacts, and limit stakeholder engagement. The bow tie framework makes these gaps visible, drawing attention to risks that remain hidden within fragmented knowledge and governance systems. Findings from the Colorado case study indicate that current CCUS governance lacks consistent mechanisms to define, measure, and account for water use and impacts across institutions. The analysis highlights a critical gap in how water is conceptualized and measured, particularly where it may be permanently removed or altered through subsurface injection, storage, or disposal in ways that do not align with conventional distinctions between consumptive and non-consumptive use. The method provides a practical tool for local and regional governments to identify risks, engage broader stakeholders, and support coordinated, interdisciplinary, and adaptive decision-making.

PMID:
42348172
Bibliographic data and abstract were imported from PubMed on 25 Jun 2026.

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