# Shadow Fleets: A Growing Challenge in Global
## 2 Methodology  
The paucity of academic literature specifically addressing shadow fleets reflects both the clandestine nature of these operations and their relatively recent prominence in global maritime affairs. Our temporal framework focuses on 2022-2024, capturing the dramatic shift in shadow fleet operations following the implementation of the Russian Oil Price Cap in December 2022. This period documents both the rapid expansion of shadow fleet capabilities and the evolution of evasion tactics from individual vessels to coordinated networks. To mitigate the limitations imposed by the scarcity of academic sources, this research relied heavily on primary sources, expert consultations, and contemporary case studies. While this lacuna in prior research presents certain constraints, it simultaneously offers an opportunity for this study to make a substantive contribution to the nascent field of shadow fleet analysis.  
To elucidate the intricate operations of shadow fleets and their covert maritime activities, we developed a comprehensive and methodical approach that synthesizes historical analysis, contemporary case studies, and cutting-edge technological insights. This dual-faceted methodology integrates a thorough examination of the extant scholarly literature with detailed case studies, complemented by an extensive data collection process enhanced by modern technological advancements. Throughout this process, we incorporated the invaluable perspectives of subject matter experts. This multidimensional strategy facilitated a profound examination of shadow fleets, illuminating their operational methodologies, strategic objectives, and the technological innovations that underpin their covert operations.  
_Integrated Methodological Approach: Literature Review, Case Studies, and Data Analysis_  
The selection and analysis of case studies followed a rigorous methodological framework designed to ensure comprehensive coverage of shadow fleet operations while maintaining analytical rigor. Our research drew from an extensive database of vessels identified as operating in dark or gray fleet capacities between 2022 and 2024. The selection methodology considered multiple vessel types and operational modalities, requiring verified documentation and data to support our analysis. From this comprehensive dataset, we selected representative cases for detailed presentation that best exemplify the distinctive characteristics of dark and gray fleets. These cases were chosen based on the clarity of documentation, completeness of tracking data, evidence of multiple evasion tactics, and their ability to demonstrate the typical operational patterns observed across the larger dataset.  
Our analysis integrated a comprehensive data collection framework incorporating both primary and secondary sources. Our primary sources included satellite imagery analysis and AIS tracking data. These were complemented by secondary sources encompassing industry reports, academic literature, investigative journalism, expert interviews, and government documentation. This multi-layered approach enabled the thorough verification and cross-referencing of our findings.  
The analytical protocol proceeded through several distinct phases. The initial case identification emerged from a systematic review of media reports, industry alerts, regula tory enforcement notices, and expert recommendations. From our database, we identified the _M/V Wise Honest_, _ANITA_, and _OLIVE_ as archetypal examples of dark fleet operations, demonstrating comprehensive regulatory evasion. For gray fleet operations, we selected _ADVANTAGE ANGEL_ and _STI DUCHESSA_, as they exemplify the nuanced approach of maintaining partial compliance while engaging in sanctioned trade. The verification process involved the rigorous cross-referencing of multiple independent data sources, coupled with expert consultation and comprehensive documentation review. This verification phase proved crucial in establishing the validity of the identified patterns and operational characteristics.