- Title
- Adopting Different Wind-Assisted Ship Propulsion Technologies as Fleet Retrofit: An Agent-Based Modeling Approach
- Creator
- Chica, Manuel; Hermann, Roberto Rivas; Lin, Ning
- Relation
- Technological Forecasting and Social Change Vol. 192, Issue July 2023, no. 122559
- Publisher Link
- http://dx.doi.org/10.1016/j.techfore.2023.122559
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2023
- Description
- The maritime shipping industry will increasingly switch to low carbon fuels and adopt energy saving technologies (ESTs) to achieve the industry target of decarbonization. Among ESTs, deck equipment, including those based on wind propulsion technologies (WPTs), represents the largest potential fuel savings and a source of increasing innovation initiatives by industry actors. Previous contributions to WPT innovation have addressed barriers and drivers for increased adoption in the industry but failed to consider the specific aspects of the fleet retrofitting market. Through an agent-based simulation model, this work studies the effects of different policy and market scenarios (subsidies, fuel prices, and networking) on the adoption of WPT retrofitting solutions. The proposed model incorporates two decision steps for each vessel to adopt the technology (acquiring awareness of the technology, and a utility decision process to determine the WPT option). The study also expands on previous knowledge by modeling three WPT options and by integrating real world data of technology costs and their fuel savings as well as vessel features. Insights from simulations allow to identify the most convenient policies as well as the potential of alternative models to reduce introduction barriers (e.g., product-service business models).
- Subject
- eco-innovation; environmental policies; shipping; retrofitting; wind-assisted propulsion technologies; agent-based modeling; SDG 7; SDG 9; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1480201
- Identifier
- uon:50455
- Identifier
- ISSN:0040-1625
- Rights
- © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
- Language
- eng
- Full Text
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