Characteristics of the shadow fleet phenomenon in crude oil shipping: A quantitative analysis of vessel characteristics, governance attributes, and sanctions-risk classification

This study presents the first vessel-level quantitative analysis of the crude-oil shadow fleet, applying binary GLM, ordered logistic, and ridge-penalised regression to 654 tankers classified by S&P Global Market Intelligence. Grounded in institutional theory and regulatory arbitrage, the analysis tests whether governance attributes outperform physical characteristics as determinants of sanctions-risk banding. Results confirm that flag state and ultimate beneficial owner jurisdiction are the primary risk predictors, whilst deadweight tonnage loses explanatory power once governance controls are included. Mid-aged vessels (built 2000–2015) exhibit elevated risk profiles consistent with a documented post-2022 acquisition wave. The binary GLM achieves an AUC of 0.965 and a McFadden R 2 of 0.670; the ridge logistic model yields a cross-validated AUC of 0.937. Marginal effects from the ordered logit provide directly usable probabilistic risk differentials for each governance attribute. Findings support governance-based screening frameworks over traditional size-anchored instruments for maritime sanctions compliance.